41_Monetary Policy Transmission Mechanism in Zambia

Bank of Zambia

Working Paper

WP/01/2014

Monetary Policy Transmission

Mechanism in Zambia**

Patrick Chileshe, Francis Z. Mbao, Brenda Mwanza, Ladslous Mwansa, Tobias

Rasmussen, and Peter Zgambo

Abstract

This study seeks to determine the relevance and magnitude of the different channels of the

monetary transmission mechanism in Zambia. Autoregressive methods are used to

empirically identify the impact of monetary policy on macroeconomic outcomes. The results

indicate that the direct link from interest rates to output and inflation in Zambia has

historically been weak and that monetary aggregates have, at least until recently, had a

greater role in the monetary transmission mechanism.

Disclaimer: The views expressed in this paper are those of the authors and do not reflect the

official position of the Bank of Zambia.

** This paper done in collaboration with the International Monetary Fund (IMF) Resident

Office

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Table of Contents I. Introduction ........................................................................................................................... 3

II. Review of Theoretical Literature on the Monetary Transmission Mechanism .................... 4

A. Interest Rate Channel ....................................................................................................... 5

B. Exchange Rate Channel ................................................................................................... 5

C. Credit Channel.................................................................................................................. 6

D. Asset Price Channel ......................................................................................................... 7

E. Expectations Channel ....................................................................................................... 7

III. Review of Empirical Literature on the Monetary Transmission Mechanism ..................... 8

IV. Evolution of Monetary Policy and Economic Variables since 1964 .................................. 9

V. Model Estimations ............................................................................................................. 12

A. Links Between Interbank and Lending Rates ................................................................ 13

B. Broader Linkages between Variables in Monetary Policy Transmission ...................... 14

Granger Causality Tests .................................................................................................. 14

VAR Model Estimation .................................................................................................. 16

VI. Conclusions....................................................................................................................... 22

REFERENCES ....................................................................................................................... 24

APPENDIX 1: ......................................................................................................................... 29

APPENDIX 2 .......................................................................................................................... 31

Table A1: Lending Rates and Interbank Rate Estimations, Entire Sample Period ......... 31

Table A2: Lending Rates and Interbank Rate Estimations, High Volatility Era ............ 31

Table A3: Lending Rates and Interbank Rate Estimations, Low Volatility Era ............. 32

APPENDIX 3 .......................................................................................................................... 33

Table A4: Pairwise Granger Causality with Policy Rate, 2012M4-2013M9 ................. 33

Table A5: Granger Causality Tests, 1995M1-2013M9 .................................................. 33

APPENDIX 4 .......................................................................................................................... 36

Figure A1: Model 2-Impulse Response .......................................................................... 36

Table A6: Model 2-Variance Decomposition ................................................................. 37

Figure A2: Model 3-Impulse Responses......................................................................... 38







Table A10: Model 6-Variance Decomposition ............................................................... 45

Figure A6: Model 1 (Early Sub-period)-Impulse Responses ......................................... 46

Table A11: Model 1 (Early Sub-period)-Variance Decomposition ................................ 47

Figure A7: Model 1 (Late Sub-period)-Impulse Responses ........................................... 48

Table A12: Model 1 (Late Sub-period)-Variance Decomposition ................................. 49

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I. INTRODUCTION

The aim of this study is to strengthen the understanding of how monetary policy, and in

particular interest rate setting, affects the Zambian economy. Effective monetary policy

depends on a central bank having a firm understanding of the link between its actions and its

objectives. The main objective of the Bank of Zambia (BoZ) is to ensure price and financial

system stability in Zambia. This objective has traditionally been pursued by targeting

monetary aggregates. In light of changes in the economy and to better anchor inflation

expectations, BoZ is now in the process of shifting its monetary policy framework to

targeting interest rates. A key step in this direction was the introduction of the BoZ Policy

Rate in April 2012. Uncovering how the Zambian economy responds to changes in interest

rates has accordingly gained importance.

The effect of interest rate setting on the broader economy works through what is termed the

monetary transmission mechanism (MTM): the process through which monetary policy

decisions are transmitted into economic activity and prices. This process is one that links

central banks’ operational targets (typically short term interest rates or reserve money) to its

intermediate targets (medium and long-term interest rates, broad money, credit, and exchange

rate) and eventually to its goal targets (inflation and output). The literature on the monetary

transmission mechanism identifies five main channels, which are discussed in detail in

Section III.

The aim of this study is to determine the relevance and magnitude of the different channels of

the MTM in Zambia. To do so, the analysis takes an empirical approach, building on earlier

work of, among others, Simatele (2004), Mutoti (2006), and Baldini et al. (2012. These

studies all support the presence of the credit and exchange rate channels in Zambia, with the

latter also identifying the presence of the interest rate channel. In addition to including more

recent data, this study builds on the previous litterature by evaluating differences in the

effectiveness of interest rates and monetary aggregates in the MTM, and it also seeks to

evaluate how these relationships may have changed over time.

While the ultimate aim of this research is to understand how a change in the BoZ policy rate

can be expected to influence the economy, a key challenge is that the policy rate has not been

in existence for very long. Up to now, the policy rate has only been changed a few times and

the scope for uncovering patterns in the way the economy has responded is correspondingly

limited. The analysis therefore looks at two other short-term interest rates, the interbank rate

and the 90 days T-bill yield rate, which can be seen as proxies for the policy rate. It then

examines how changes in these two interest rates have impacted on the central bank’s

intermediate and ultimate targets.

The main finding is that the direct link from interest rates to output and inflation in Zambia

has been weak and that monetary aggregates have, at least until recently, had a greater role in

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the MTM. While the exchange rate channel is clearly present and there is also some evidence

to suggest that interest rates are gaining in importance compared to monetary aggregates, it is

therefore still too early to abandon the traditional policy focus on monetary aggregates.

Instead, an important objective should be to enhance the MTM to enhance to effectiveness of

monetary policy whether delivered via monetary aggregates or through the BoZ policy rate.

The rest of this paper is organized as follows. Sections II and III provide a review of the

theoretical and empirical literature. Section IV outlines the evolution of the Zambian

economy since the early 1960s. Section V presents a series of models and estimations.

Section VI concludes.

II. REVIEW OF THEORETICAL LITERATURE ON THE MONETARY TRANSMISSION

MECHANISM

The MTM determines how policy-induced changes in the nominal money stock or short-term

interest rates impact on output and inflation (Ireland, 2005). The neo-classical view of long-

run neutrality of money is widely accepted. Nevertheless, monetary policy is in the short run

thought to influence economic activity through changes in interest rates or money supply,

either because of nominal price rigidities (Keynesian view) or owing to a number of wealth,

income, liquidity, and expectation effects (Dabla-Norris and Floerkemeier, 2006). Although

different classifications have been made and there is some overlap, the theoretical literature

identifies five main channels of the MTM. These are the interest rate, exchange rate, bank

lending, asset price, and expectations channels (Bank of England, 1999; Horvath et al., 2006;

Loayza et al., 2002; Mishkin, 1995; Obstfeld et al.,1995; Taylor, 1995). The transmission

channels are graphically illustrated in Figure 1 and described below.

Figure 1: Monetary Policy Transmission Channels

Source: Adapted from Loayza et al., (2002) and Bank of England (1999).

Source: Adapted from Loayza et al.(2002) and Bank of England (1999).

Monetary

policy

instrument

Interest rates

Credit

Asset prices

Exchange

rates

Domestic

demand

External

demand

Total

demand

Domestic

inflationary

pressure

Import

prices

Inflation

Expectations

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A. Interest Rate Channel

The interest rate channel is considered the primary MTM in traditional models that operate

by altering the marginal cost of lending and borrowing and thereby produce changes in

investment, saving, and aggregate demand (Horvath at al., 2006). Key to the interest rate

channel is the notion that if prices are sticky then central bank actions that change nominal

short term interest rates also change real interest rates and ultimately output. In this setting,

an expansionary monetary policy leads to a fall in real interest rates, which in turn stimulates

investment due to a decline in the cost of borrowing. An increase in investment leads to an

increase in aggregate demand and output, which may in turn result in increased inflationary

pressures in the economy.

The basic functioning of the interest rate channel has remained relatively unchanged as the

literature on monetary transmission has evolved. The mechanism is still present in theories

that incorporate expectations (Butkiewicz and Ozgdogan, 2009; Clarida, Gali and Gertler,

1999; Rotemberg and Woodford, 1998). Moreover, other studies have extended the

Keynesian focus on business investment to include effects on spending on housing and other

consumer durables as well as substitution effects in consumption spending (Butkiewicz and

Ozgdogan, 2009; Els, Locarno, Morgan and Viletelle 2003; Taylor, 1995).

While changes in the central bank’s policy rate are expected to be immediately transmitted to

short-term money market rates, several factors influence the effectiveness of the interest rate

channel. These include the structure and competiveness of banking sector, the size of the

shadow informal sector, and the speed with which the policy rate is transmitted to

commercial lending rates (Bank of England, 1999; Tahir, 2012; Horvath et al., 2006; Dabla-

Norris, 2012).

B. Exchange Rate Channel

The exchange rate channel works through the effect that monetary policy, via the exchange

rate, has on imports and exports (Horvath et al., 2006). Monetary policy can influence the

exchange rate through interest rates via the uncovered interest rate parity (UIP) condition,

through direct intervention in foreign exchange markets, or through inflationary expectations

(Dabla-Norris et al., 2006).

The link between monetary policy and exchange rate under the UIP condition was

popularized by the open macroeconomic models developed by Fleming (1962), Mundell

(1963), and Dornbusch (1976). Under the UIP, the difference between the domestic and

foreign interest rate is equal to the expected exchange rate change. Accordingly, monetary

policy induced changes in domestic interest rates therefore change exchange rate

expectations and hence the relative price of imports and exports, which in turn affects

aggregate demand and supply. On the demand side, monetary policy that lowers domestic

interest rates will cause the currency to depreciate, making exports cheaper and imports more

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expensive increasing net exports and hence impacting positively on aggregate demand and

output (Obstfeld and Rogoff, 1995; Taylor, 1993; Mishkin, 1996, 2001). On the supply side,

however, the higher domestic price of imported goods increases inflationary pressure and

contracts output (Chipili, 2013; Ozdogan, 2009; Kara, Tuger, Ozlale, Tuger, Yavuz and

Yucel, 2005; Campa and Goldberg, 2004; Alper 2003; Loazya and Schmidt-Hebbel, 2002;

McCallum and Nelson, 2001).

The effectiveness of the exchange rate channel in the transmission mechanism depends on

the extent of the pass-through to domestic prices, which in turn depends on the import share

and other characteristics of the economy. In general, the larger the import share or the more

the economy is dollarized, the larger the exchange rate pass-through (Horvath et al., 2006).

C. Credit Channel

Benanke and Gertler (1995) propose the bank lending (credit) channel, which explains the

MTM as an outcome of credit market imperfections arising from asymmetric information and

costly enforcement of contracts in financial markets. The basic notion underlying this

channel is that monetary policy can have price and output effects through credit rationing that

arises from information asymmetries between financial institutions and the firms and

consumers to which they lend (Loayza et al., 2002). This occurs because monetary policy

affects the extent of adverse selection and moral hazard that constrain the provision of credit

in the economy. It is argued that monetary expansion reduces adverse selection and moral

hazard problems by increasing firm’s net worth, reducing perceived loan risks, improving

firms’ cash flow, and decreasing the burden of nominal debt contracts (Loayza et al., 2002).

The credit channel has two sub-channels—the bank lending and the balance sheet channels.

The bank lending sub-channel works by influencing banks’ ability to make loans following

changes in the monetary base (Kishan and Opiela, 2000; Kashyap and Stein, 2000; Huang,

2003; Sichei, 2005). Here, a policy induced expansion of the monetary base increases the

amount of reserves (deposits) available to banks, which they can use to advance loans. An

expanded monetary base is thus likely to increase lending for investment and consumption

purposes, leading to a rise in investment and consumption spending. The increase in

domestic demand raises aggregate demand and, if aggregate demand exceeds aggregate

supply, also inflationary pressures in the economy.

The balance sheet sub-channel is premised on the prediction that the external finance

premium that a borrower faces depends on the borrower’s net worth. In this regard, monetary

policy can have direct and indirect effects on borrowers’ balance sheets. A direct effect arises

when an increase in interest rates works to raise the payments a borrower must make to

service debts, while an indirect effect arises when interest rates reduce the capitalized value

of the borrower’s assets (Ireland, 2006). As a result, an increase in interest rates arising from

tight monetary policy depresses spending through the traditional interest rate channel, but

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also raises the borrowers’ cost of capital through the balance sheet channel. This reduces

investment, consumption, employment, and output, and puts downward pressure on prices.

Factors that strengthen the credit channel include the magnitude of bank capitalization, the

degree of development of the securities markets, and the size of firms in the economy

(Putkuri, 2003, and Tahir, 2012).

D. Asset Price Channel

The asset price channel is premised on the idea that monetary policy can have important

effects on prices of assets such as bonds, equity, and real estate (Bank of England, 1999;

Loayza et al., 2006). As noted by Horvath et al. (2006), the asset price channel operates

through changes in the market value of firms and the wealth of households. Here, changes in

monetary policy alter the relative price of new equipment, thereby affecting firms’

investment spending and their market value. Changes in monetary policy also affect

households’ collateral for borrowing, thereby affecting consumption spending.

According to the theory of the asset price channel, expansionary monetary policy results in

higher equity prices because the expected future returns are discounted by a lower factor,

thereby raising the present value of any given future income stream. Higher equity prices

makes investment more attractive (e.g., through Tobin’s q), which raises aggregate demand.

Higher equity prices also entail increased household wealth, which raises consumption and

aggregate demand (Loayza et al., 2006). In turn, the changes in aggregate demand impact on

output and inflation.

Tahir (2012) highlights the following factors as key to the strength of the asset price channel:

the degree of household participation in the capital market; the prevalence of firms that raise

funds through issuance of shares; and the level of development of the national stock market.

This is confirmed by Kamin et al. (1998) and by Butkiewicz and Ozgdogan (2009) who note

that the asset price channel in developing and emerging markets is weak and more

unpredictable compared to developed economies due to shallower and less competitive

markets as well as more unstable macroeconomic environments.

E. Expectations Channel

The four monetary transmission channels noted above focus on money and asset markets.

However, the literature also identifies a fifth channel, which is based on the private sector’s

expectations about the future stance of monetary policy and related variables (Loayza et al.,

2002). This reflects the notion that monetary policy changes can influence expectations about

the future course of real activity and the confidence with which those expectations are held.

Changes in perception will then affect the behaviour of participants in financial markets and

other sectors of the economy through, for instance, changes in expected future labour

income, unemployment, sales, and profits (Bank of England, 1999).

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Although the importance of expectations is well established, the direction in which the effect

will work is not easy to predict. For example, the Bank of England (1999) notes that an

increase in the policy rate may lead economic agents to think that the monetary authorities

believe that the economy is likely to be growing faster than previously thought, giving

expectation of future growth and confidence in general. There is, however, also the

possibility that economic agents interpret a rate hike as signalling a perceived need by the

monetary authority to slow growth to achieve the inflation target, which would impact

negatively on growth expectations and confidence. Hence, depending on how expectations

are formed, the impact of monetary policy change could be very different.

In the following section, we review the empirical literature on the monetary transmission

mechanism, focusing on Africa and the Zambian economy in particular.

III. REVIEW OF EMPIRICAL LITERATURE ON THE MONETARY TRANSMISSION

MECHANISM

Vast empirical work has been undertaken on the MTM in both developed and developing

countries. In developed economies, all the above-listed channels from the theoretical

literature have been found to play some role in the transmission of monetary policies. In

developing economies, however, empirical studies have found rather limited roles for some

of the channels—as could be expected from these countries’ relatively small banking

systems, shallow financial markets, and weak institutional frameworks (Mishra et al. 2010;

Al-Mashat et al., 2007; Bakradze et al., 2007; Dabla-Norris et al., 2006; Egert et al., 2006;

Smal et al., 2001; Montiel, 1990).

Studies looking at Africa suggest that, in this part of the world, the interest rate channel is

generally weak, and that the credit and exchange rate channels are more important although

not always very strong. In particular, Buigut (2009) found the interest rate channel to be of

relatively little importance in the transmission of monetary policy to output and prices in the

East African region. Al-Mashat et al. (2007) found similar results for Egypt.1 Chibber and

Sharik (1990) found that the credit channel was via money creation the main transmission

channel in Ghana. Caneti and Green (1991) analysed the impact of monetary growth and

exchange rate developments in ten African countries and found that though both factors were

important in the inflationary process in most of the countries examined, neither money nor

exchange rate developments had a dominant role.

1 For South Africa, a number of empirical studies have identified the interest rate channel as the dominant

channel (Aron et al., 2000; Smal et al., 2001), likely reflecting the more the advanced nature of the financial

sector compared to other African economies.

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Studies looking specifically at Zambia point to a relatively important role for the exchange

rate channel. Using error correction and vector autoregression (VAR) methodologies,

Mwansa (1998) found the exchange rate to have a significant and much stronger impact on

inflation than money growth. Mutoti (2006), reached a similar conclusion by employing a

cointegrated structural vector autoregression methodology. This second study further

established that the impact of money supply shocks on Zambia’s output tended to be small

and temporary, and that such shocks have had little bearing on inflation dynamics,

particularly in the long-run. A third study by Simatele (2003) used a VAR methodology and

also found the exchange rate channel to be the most important and further concluded that

bank lending in Zambia is not driven by monetary policy but rather by demand. A forth and

more recent study by Baldini (2012), which used a dynamic stochastic general equilibrium

model, confirmed the presence of the exchange rate channel but also pointed to a role for the

credit channel.

IV. EVOLUTION OF MONETARY POLICY AND ECONOMIC VARIABLES SINCE 1964

The conduct of monetary policy in Zambia can be divided into two broadly distinct periods:

The pre-liberalization period spanning from 1964 to 1991 and the post-liberalization period

from 1992 on.

During the first period, prior to 1992, monetary policy had multiple and poorly defined

objectives and its implementation relied mainly on direct instruments. The latter included

controlled interest rates and directed credit allocation, as well ratios on core liquid assets and

statutory reserve requirement. The dependence on these direct instruments originated in the

realization by the newly independent central bank that it had little control over money

supply. As noted by Kalyalya (2001), despite the central bank being empowered through the

Bank of Zambia Act of 1965 to implement monetary policy, BoZ had little grip on the

growth of credit in the economy. The banking sector was dominated by foreign banks who

issued loans to mostly foreign owned companies with little regard to domestic economic and

financial conditions. Further, during much of this period, the financing of the government

budget relied heavily on borrowing from the central bank.

Macroeconomic conditions deteriorated steadily during this period. The persistent use of

central bank financing by government as well as failure of the monetary authority to control

money supply resulted in growing inflationary pressure (Bigstern and Mugerwa, 2000).

These problems were further compounded by internal and external imbalances as well as

structural and institutional deficiencies. Domestically, price controls on most food items,

widespread consumer subsidies, and the industrialization strategy of import substitution

coupled with weak public administration worsened the fiscal position and led to a highly

inefficient allocation of resources On the external front, the country’s balance of payment

position became unsustainable following the loss of international reserves due to growing

10

foreign debt servicing and dwindling export earnings resulting from falling prices and output

of copper.

The combined effect of the factors above pushed the economy to stagnation and near

hyperinflation (Table 1 and Figures 2-3). Annual economic growth fell from an average of

3.9 percent during 1961-65 to 1.1 percent during 1981-90. At the same time, external debt as

percentage of GDP rose from 49 to 119 percent of GDP. Inflation reached an average of 76.9

percent during the 1980s, and with negative real interest rates, the banking system started to

lose its intermediation role and credit to the private sector declined relative to GDP.

The second period, staring in 1992, began as a new government came into power with an

agenda to restore economic growth through market-based stabilization policies and

promotion of the private sector. Market forces were given greater role in the allocation of

resources as prices were decontrolled and most subsidies abolished.

Table 1: Evolution of Key Monetary and Economic Variables

Source: World Bank Database, and BoZ database.

Note: The real interest rate is the lending interest rate adjusted for inflation as measured by the GDP deflator.

Changes in the economic environment carried through to the conduct of monetary policy.

The amended Bank of Zambia Act No. 43 of 1996 narrowed down the central bank’s

objective to price and financial system stability. Consequently, monetary policy concentrated

on creating a stable macroeconomic environment to support sustainable economic growth.

Under the new framework, BoZ started to target monetary aggregates, an approach premised

on a strong relationship between the ultimate target (inflation) and money supply.

In its conduct of monetary policy, BoZ increasingly relied on indirect rather than direct

instruments. The indirect instruments included primary auctions of treasury bills and

government bonds, as well as auctions of short-term credit and term deposits to and from

commercial banks. In addition, the central bank’s purchases and sales of foreign exchange

were used as a tool of monetary policy. With these indirect instruments, the BoZ tried to

Indicator Name 1961-1970 1971-1980 1981-1990 1991-2000 2001-2010 2011 2012

Real Per Capita GDP Growth (annual % growth) 0.8 -1.9 -1.8 -1.7 2.8 3.6 4.0

Real GDP Growth (annual % growth) 3.9 1.5 1.1 0.8 5.6 6.8 7.3

Average Annual Inflation Rate - 11.1 76.9 68.1 15.5 6.4 6.6

External Debt Stocks (% of GNI) - 75.3 206.1 214.3 89.9 27.4 27.6

External Debt(% of GDP) - 48.7 119.3 147.3 67.9 18.1 19.0

Total Debt Service (% of exports ) 2.9 26.2 25.1 25.0 12.9 2.2 2.2

Total Reserves (% of total external debt) - 10.1 2.8 2.8 23.1 47.0 56.5

Total Reserves (% of GDP) 18.6 7.1 4.5 5.0 9.1 12.1 14.7

Broad Money (% of GDP) 19.3 29.0 30.9 18.2 21.3 23.4 24.1

Broad Money Growth (annual % growth) 27.2 10.5 41.5 49.9 22.7 21.7 17.9

Real Interest Rate (%) - 0.8 -15.5 3.1 11.3 5.6 5.6

Domestic Credit (% of GDP) -0.3 41.9 63.9 59.6 28.2 18.1 18.5

Domestic Credit to Private Sector (% of GDP) 8.5 17.1 14.0 7.5 9.6 12.3 14.8

External Balance (% of GDP) 15.1 0.9 -1.7 -6.9 -2.4 9.0 -

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influence the behavior of financial institutions and other market players through market

mechanisms rather than relying on direct instruments, as it had before. This helped improve

control of money supply and inflation and also promoted a more efficient allocation of credit

and financial market development in general.

The change in the monetary policy framework and its implementation contributed to a

marked improvement in Zambia’s macroeconomic environment. Money growth and inflation

declined sharply, with the latter being held in the single digits since 2006. The liberalization

of lending and deposit rates initially caused real interest rates to spike but they subsequently

leveled off at about 7 percent. Moreover, since the late 1990s, real GDP growth steadily

increased, reaching an average of 7.2 percent a year during 2010-12.

Figure 2: Trends in Real Growth and Real Interest Rates since 1971

(Three-year moving averages, in percent)

Source: BoZ database and computations by authors.

The task of maintaining price stability as the economy evolved was not without challenges

for BoZ. In particular, the relationship between money growth and inflation has weakened as

the two have declined. In response, the BoZ started to move towards targeting inflation rather

than monetary aggregates. A key step in this direction was the introduction of the BoZ policy

rate in April 2012. This policy rate is reviewed each month with a view to price

developments, and BoZ has been intervening in the money market if the interbank rate

slipped out of a corridor set at +/-2 percent from the policy rate.

-40

-30

-20

-10

0

10

20

-4

-2

0

2

4

6

8

1971 1976 1981 1986 1991 1996 2001 2006 2011

Real GDP growth (LHS)

Real interest rate (RHS)

12

Figure 3: Trends in Broad Money Growth and Inflation since 1971

(in percent)

-40

0

40

80

120

160

200

1975 1980 1985 1990 1995 2000 2005 2010

Annual Broad money growth

Annual CPI Inflation

Post-1995 correlation=0.52Pre-1995 Correlation=0.70

Source: BoZ database and computations by authors.

V. MODEL ESTIMATIONS

This section presents a number of estimations carried out to uncover the monetary

transmission mechanism in Zambia. First, we examine the interest rate channel by looking at

the link between interbank and lending rates—a key component of interest rate pass-through

to the wider economy. Next, we bring in a wider set of variables to examine the full set of

possible channels, using pair-wise Granger causality tests and VAR models to identify

linkages.

The estimations are carried out on monthly and quarterly data spanning January 1995 to

October 2013. For Zambia, the data series cover interest rates (interbank, average lending

and deposit, and 90-day T-bills), money supply, exchange rates, credit, consumer prices,

industrial production, and electricity generation. In addition, the US federal funds rate,

international oil prices, and an international commodity price index were used in some

specifications. Details on the data are provided in Appendix 1. In the absence of high

frequency data on real GDP, we use the monthly series on electricity generation and the

quarterly series on industrial production as proxies for overall economic output.

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A. Links Between Interbank and Lending Rates

Even a quick glance at the data reveals that the link between interbank and lending rates in

Zambia has historically been weak, especially in the recent past. Figure 4 shows that

interbank rates during 1995-2006 were much more volatile than lending rates, although the

two were positively correlated. After 2006, the volatility of the interbank rate diminished,

and the level of both these interest rates has also been much lower, but the correlation

between the two series has been negative.

Figure 4: Lending and Interbank Rates, Jan. 1995 – Oct. 2013

0

10

20

30

40

50

60

70

1996 1998 2000 2002 2004 2006 2008 2010 2012

Lending Rates (lr)

Inter-Bank Money Market Rates (ibr)

Len

din

g a

nd

In

ter-

ban

k R

ates

(%

)

Period of high interest rates volatility:

stdv (lr) = 9.21

stdv (ibr) = 13.3

Positive correlation:

r = 0.66

Low interest rates volatility era:

stdv (lr) = 4.42

stdv (ibr) = 3.60

Negative correlation:

r = - 0.24

Following Mishra et al. (2010) we investigate the relationship between the interbank and the

lending rate by adopting the following model:

where, lr is the lending rate and ibr is the interbank rate. Here the short-term is effect is given

by while the long term effect is given by (

The results of this estimation are summarized in Table 2 where they are also compared to the

findings for other groups of countries obtained from Mishra et al. (2010).2 While the

estimations for Zambia suggests the presence of positive long-run effects going beyond the

short-term impact, both the short-term and long-term effects are much smaller than in the

other country groupings. In Zambia, based on the full sample period, a one percentage point

2 See Appendix 2 for the detailed estimation results.

14

increase in the interbank causes the lending rate to increase by 2 basis points in the short run

and 13 basis points in the long run, much less than the corresponding 37 and 58 basis point

increases seen for emerging markets as a whole. Moreover, consistent with the correlations

identified in Figure 4, both the short and long-run effects in Zambia are found to have been

smaller in the period since 2006 than in the preceding period.

Table 2: Interest Rates Pass-through from Interbank Rates to Lending Rates

Description Short Run

Effects

Long Run

Effects

R-Squared

Advanced Economies 0.20 0.36 0.41

Emerging Markets 0.37 0.58 0.65

LICs 0.10 0.30 0.16

Zambia: Jan 1995 – Oct 2013

Jan 1995 – Jan 2006

Feb 2006 – Oct 2013

0.02 0.13 0.20

0.02 0.13 0.21

-0.01 0.04 0.16

Source: Mishra et al. (2010) and author’s computations,

The results in Table 2 highlight the challenge in moving to a monetary policy framework

where the policy rate is via the interbank rate meant to influence the wider Zambian

economy. Based on this evidence, monetary policymakers should, even with full control over

the interbank rate, not expect to have much control over broader macroeconomic outcomes

unless the interbank rate becomes more effective at determining lending rates than has been

the case so far.

B. Broader Linkages between Variables in Monetary Policy Transmission

Granger Causality Tests

As a first step to uncovering the wider set of variables that may be important for the MTM in

Zambia we perform pair-wise Granger causality tests. The Granger (1969) approach to the

question of whether X causes Y is to see how much of the current value of Y can be

explained by past values of Y and then to see whether adding lagged values of X can

improve the explanation. Here, Y is said to be Granger-caused by X if X helps in the

prediction of Y, or equivalently if the coefficients on the lagged values of X are statistically

significant. Specifically, the null hypothesis is that X does not cause Y and if the null

hypothesis is rejected then it implies that X may cause Y.

The results for the Granger causality test are summarized in Tables 3 and 4, with the full

estimation details shown in Appendix 3. The following patterns in interrelationships emerge

in relation to interest rates and monetary aggregates—the variables that are most closely

related to monetary policy:

15

Links between the policy rate and other interest rates. There is only weak evidence of

causality running from the policy rate to any of the other interest rates or from any of

these other rates to the policy rate. The results in Table 3 suggest possible causality

from the policy rate to the T-bill rate and from the interbank and deposit rates to the

policy rate, but these are all only significant at the 10 percent level. An important

limitation here, however, is that these tests are based on a very short sample size

starting in April 2012 when the policy rate was introduced.

Table 3: Pairwise Granger Causality Tests: Policy rate

Policy Rate

Y \ X (X causes Y) (Y causes X)

T-Bill Rate *

Interbank Rate *

Lending Rate

Deposit Rate *

Source: Author's calculations.

Note: "*", "**", and "***"indicate Granger Causality at significance level

of, respectively, 10, 5 and 1 percent.

Links from market interest rates to other variables. The results in Table 4 suggest that

the different market interest rates all influence each other, except the T-bill rate,

which does not seem to be influenced by any of the other interest rates, and the

interbank rate, which does not appear influenced by the deposit rate. Moreover, none

of the market interest rates appear to have a direct causal impact on reserve or broad

money, inflation, or output as proxied by electricity generation. In contrast, all the

market interest rates appear to have a significant impact on credit to the private

sector, and the T-bill and interbank rates also appear to influence the NEER.

Links to short-term interest rates from other variables. Evidence of causality running

to market interest rates depends on the interest rate in question. The interbank rate

appears to be influenced by all the other variables under consideration, but results for

the other interest rates are sporadic. However, the findings suggest that all the

different market interest rates are influenced by inflation as well as by credit to the

private sector, and also that broad money influences the T-bill as well as the interbank

rate.

Links from monetary aggregates to and from other variables. Broad money is found

to influence all variables except lending and deposit rates, while reserve money only

influences the interbank rate, inflation, and electricity generation. Reserve money is

influenced by broad money and output (as proxied by electricity generation), but

broad money is not influenced by any other variable.

16

Other links to inflation and output. Inflation is influenced by the NEER, credit to the

private sector, and output (as proxied by electricity generation). Moreover, output is

influenced by reserve and broad money as well as credit to the private sector.

Table 4: Pairwise Granger Causality Tests: Full Sample

(X Granger-Causes Y)

Y \ X (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

T-Bill Rate (1) - * ** *

Interbank Rate (2) *** - ** *** *** *** *** *** **

Lending Rate (3) *** ** - *** *** *

Deposit Rate (4) *** *** *** - *** ***

Reserve Money (5) - *** *

Broad Money (6) -

Inflation (7) ** *** - ** *** **

NEER (8) ** *** * ** -

Credit to Private Sector (9) ** *** *** *** * * -

Electricity Generation (10) * *** ** -

Source: Author's calculations.

Note: "*", "**", and "***"indicate Granger Causality at significance level of, respectively, 10, 5 and 1 percent.

The above findings enable some preliminary conclusions about the MTM channels in

Zambia. First, in as far as monetary policy works though interest rates, it appears to impact

the goal variables of inflation and output only via the exchange rate and credit to the private

sector. Second, where monetary policy works through reserve money, it appears to influence

inflation and output directly as well as indirectly via the interbank rate. The next section

looks further into these linkages and also examines their magnitudes.

VAR Model Estimation

Following widely used practice (Mishra et al., 2010;, Davoodi et al.,2013; Mishra and

Montiel, 2013), we assume that the impact of monetary policy on the wider economy van be

modeled in the following VAR structural form framework:

.

Here, is a nx1 vector of endogenous variables, c is a nx1 vector of constants, is a

mx1vector of exogenous variables, and is a nx1vector of error terms. A and B are nxn

and nxm matrices, which give the structure of the relationship among the endogenous and

exogenous variables in the model.

In our baseline model (Model 1), the endogenous variables are electricity production,

consumer price index (CPI), broad money (M3), interbank interest rate, and NEER. In

addition, following Sims (1992), the model includes a series of exogenous variables to

17

capture their direct impact on the economy as well as their possible influence on monetary

policy, namely the US federal funds rate, the oil price, and the index of international

commodity prices. We estimate the VAR on monthly data in log levels with 2 lags (as

determined by using lag selection criteria).

The VAR is used to investigate the impact of changes in monetary policy on the rest of the

economy by application of impulse-response and variance decomposition analysis. The

impulse response function traces the response of a variable to innovations in another variable,

and here we focus on shocks to monetary aggregates or short-term interest rates to discern

monetary policy action. The variance decomposition measures the amount of variation in a

given variable that is explained by variation in another variable at a given horizon. This gives

an indication of the degree that changes in variables closely tied to monetary policy influence

other macroeconomic variables.

The ordering of the endogenous variables in a VAR may have important bearing on the

results. We order electricity production first, on the assumption that real economic activity

responds sluggishly to policy and economic shocks. Next in the ordering is CPI, which is

assumed to respond contemporaneously to innovations in real economic activity but not in

the same period to innovations in the other variables. After that comes broad money to

indicate that it responds to prices and real economic activity. The interbank rate is ordered

after broad money to reflect that the central bank when intervening in the money market

looks to movements in broad money as well as inflation and real economic activity. The

NEER is ordered last, meaning that it responds contemporaneously to all the variables in the

model, reflecting the view that exchange rates respond more readily to changing economic

conditions than any other variable in the model. This ordering follows that used by Davoodi,

Dixit and Pinter (2013), Cheng (2006), and Buigut (2009).

To further identify the channels in the MTM and also to check the robustness of the results of

the baseline model, we examine a number of different variations to Model 1.

Model 2 uses reserve money instead of broad money, to better capture what is under

central bank control.

Model 3 includes private sector credit to evaluate the credit channel, placing it after

the short term interest rate and before the exchange rate in the variable ordering.

Model 4 retains the private credit variable but replaces broad money with reserve

money.

Model 5 includes the lending rate ordered after the T-bill rate to further investigate

the interest rate channel.

18

Model 6 uses quarterly instead of monthly data allowing us to replace electricity with

industrial production as a proxy for economic activity, albeit on a shorter sample size

spanning 2001Q1 to 2013Q3.

In addition to the different specifications above, we also re-estimate Model 1 over two

periods, 1995M1-2006M1 and 2006M2-2013M10 to investigate if the transmission

mechanism has changed over time.

Results

The impulse-response analysis for Model 1 is shown in Figure 5. The results indicate that a

positive shock to broad money has a statistically significant positive effect on output (as

proxied by electricity generation), consumer prices, and the NEER (meaning depreciation).

This is all as could be expected from expansionary monetary policy. There is with a short lag,

however, also a marginally significant positive impact on the interbank rate. This last effect

is not necessarily what could be expected but may reflect that greater money supply tends to

be followed by tightening of interest rate policy to counter pressure on the exchange rate and

consumer prices.

The impact of a positive shock to the interbank rate leads is much weaker than that of a shock

to money supply, with no significant impact on any of the other variables except for a

borderline significant appreciation of the NEER. This emphasizes the relative importance of

the exchange rate channel and is in line with the Granger causality results in Table 4, which

also did not uncover any significant impact of the interbank rate directly on output or

inflation.

The finding of a more powerful role for money supply than for interest rates in the MTM is

confirmed in the magnitude of the impulse responses. A one percent increase in M3 produces

within a time period of less than 6 months an approximately 0.6 percent depreciation, a 0.4

percent increase in output, and a 0.3 percentage point increase in the interbank rate, with the

impacts then fading out. The impact on CPI is more gradual and takes about 15 months to

reach 0.15 percent, but this effect persists. The impact of higher interest rates is much

weaker, with the effect of a one percentage point increase in the interbank rate on the NEER

peaking at about 0.15 percent within a few months.

Table 5 shows that shocks to broad money explain, respectively, 4 and 23 percent of the

forecast error variance of output and prices at the 36-month horizon. The corresponding

numbers for shocks to the interbank rate are 4 and 5 percent, respectively. This again

highlights how broad money is, or at least has been, far more important for inflation

outcomes in Zambia than is the interbank rate, and that neither has much bearing on changes

in output.

19

Figure 5: Model 1: Impulse Responses

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35

Response of LELECTRICITYG to LM3

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35

Response of LELECTRICITYG to INTERBANKR

-.008

-.004

.000

.004

.008

.012

5 10 15 20 25 30 35

Response of LCPI to LM3

-.008

-.004

.000

.004

.008

.012

5 10 15 20 25 30 35

Response of LCPI to INTERBANKR

-.01

.00

.01

.02

.03

.04

5 10 15 20 25 30 35

Response of LM3 to LM3

-.01

.00

.01

.02

.03

.04

5 10 15 20 25 30 35

Response of LM3 to INTERBANKR

-2

0

2

4

6

8

5 10 15 20 25 30 35

Response of INTERBANKR to LM3

-2

0

2

4

6

8

5 10 15 20 25 30 35

Response of INTERBANKR to INTERBANKR

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35

Response of LNEER to LM3

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35

Response of LNEER to INTERBANKR

Response to Cholesky One S.D. Innovations ± 2 S.E.

20

Table 5: Five Variables VAR-Variance Decomposition Variance Decomposition of LELECTRICITYG:

Period S.E. LELECTRICI

TYG LCPI LM3 INTERBANK

R LNEER 1 0.064412 100.0000 0.000000 0.000000 0.000000 0.000000

6 0.099237 92.07292 0.428047 4.327663 3.114020 0.057350 12 0.106138 90.68329 0.518300 4.381233 4.055932 0.361249 24 0.108353 89.81301 0.744872 4.306966 4.346807 0.788348 36 0.108808 89.50973 0.928751 4.280761 4.401548 0.879211

Variance Decomposition of LCPI:



R LNEER 1 0.010230 3.204355 96.79564 0.000000 0.000000 0.000000

6 0.029812 13.49318 78.96202 5.613721 1.663329 0.267751 12 0.041149 15.59236 69.90031 10.52548 3.699704 0.282132 24 0.055545 12.10843 64.70947 18.29044 4.676208 0.215450 36 0.066424 9.302950 62.77269 23.13447 4.637126 0.152760

Variance Decomposition of LM3:



R LNEER 1 0.035382 0.065766 0.252632 99.68160 0.000000 0.000000

6 0.067265 11.13501 3.958818 84.23527 0.417101 0.253797 12 0.082614 21.93775 3.110156 73.94242 0.328075 0.681598 24 0.094499 23.61895 6.758055 67.78651 0.301039 1.535446 36 0.100644 21.42267 11.21952 65.28756 0.342444 1.727817

Variance Decomposition of INTERBANKR:



R LNEER 1 6.185883 3.718053 6.88E-07 0.142989 96.13896 0.000000

6 7.119853 4.393583 4.878136 4.917510 85.51542 0.295346 12 7.218255 4.601294 5.687775 5.748075 83.31346 0.649400 24 7.323894 6.196782 5.953787 5.839788 81.06503 0.944613 36 7.350374 6.596998 6.028257 5.802492 80.55466 1.017598

Variance Decomposition of LNEER:



R LNEER 1 0.037947 1.123474 1.058623 16.49691 0.165800 81.15519

6 0.089239 5.363571 2.036543 16.61232 4.161282 71.82629 12 0.100089 7.735939 1.809886 15.00787 5.213176 70.23312 24 0.106470 10.78012 3.719197 14.00846 5.881308 65.61091 36 0.109490 11.47546 6.021589 14.09785 6.060299 62.34480

21

The estimations relating to the various model extensions and robustness checks are shown in

Appendix 4, with the main differences highlighted here3,4:

Model 2 (Figure A1, Table A6), where reserve money is used instead of broad

money, shows that shocks to money supply no longer have a significant impact on

any of the other variables. This supports the results from the Granger-causality tests

indicating that broad money has a much stronger bearing on the economy than

reserve money.

Model 3 (Figure A2, Table A7), which includes credit to the private sector, retains the

same general results as Model 1 but, in addition, shows that credit to the private

sector is impacted positively by an increase in money supply. There is, however, no

significant impact on credit from an increase in the interbank rate.

Model 4 (Figure A3, Table A8), which retains the credit variable but replaces broad

money with reserve money, shows similar results to Model 2, with no significant

impact from shocks to reserve money to any of the other variables.

Model 5 (Figure A4, Table A9), which includes the lending as well as the T-bill rate,

shows that while both these interest rates have a (marginally) significant impact on

the NEER, only lending rates have a significant (and negative) impact on credit. That

lending rates are found to be more important for credit than interbank rates suggests

that borrowing is price sensitive but also confirms the limited transmission from the

interbank rate seen in the other model specifications.

Model 6 (Figure A5 Table A10), which uses industrial production instead of

electricity as a proxy for output, shows similar results to Model 1, except that the

significance of the results is generally somewhat weaker. In particular, the impact of

the interbank rate on the NEER is no longer significant. Moreover, the part of the

forecast error variance explained by other variables in the model is also mostly lower

than with Model 1. However, M3 is found to have greater influence on the variance in

industrial production (13 percent at the 3-year horizon) than it did on electricity

production in Model 1.

3 To check robustness of the variable ordering, we also tried by placing CPI after M3 (i.e., electricity

generation, M3, CPI, interbank rate, and nominal effective exchange rate). The results were not significantly

different from those obtained with Model 1.

4 We also tried using copper prices instead of the broader international commodity price index. The results were

not significantly different from our main results except that the influence of the interbank rate fell slightly.

22

The results from estimating Model 1 over the two sub-periods (1995-2005 and 2006-2013)

are broadly similar to those for the whole sample period, with the main differences pointing

to a mostly diminishing role for broad money on the price level and a somewhat increasing

role for the interbank rate (Figures A6-7 and Tables A11-12). In particular, the variance

decomposition at the 36-month horizon shows that the influence of the interbank rate on the

other variables increased between the two sub-periods in all cases except for the output proxy

where the contribution of the interbank rate was essentially unchanged. Further, the

contribution of broad money to changes in other variables was in all cases higher in the

second period except for the price level where it reduced. The rising role of interest rates is

also visible in the impulse response of CPI to the interbank rate, which for the second sub-

period shows an initial—albeit only borderline significant—decline in inflation.

VI. CONCLUSIONS

The evolving Zambian economy poses new challenges for the conduct of monetary policy. In

particular, as tighter control of money supply has helped bring inflation to single digits, the

previously strong link between consumer prices and reserve money has become less clear

cut. In response, the Bank of Zambia—along with many other central banks in similar

situations—is moving to a policy framework that targets inflation via a policy rate instead of

focusing on maintaining a certain growth rate in money aggregates.

In any policy framework, ensuring stability requires a good understanding of the monetary

transmission mechanism. The empirical evidence presented in this paper suggests that the

monetary transmission mechanism in Zambia has been weak and more closely connected to

monetary aggregates than interest rates. On the whole, and in contrast to money supply,

market interest rates do not appear to have had much direct bearing on either output or

inflation. Market interest rates do appear, however, to have important bearing on the

exchange rate and to some extent also on credit to the private sector. This all points to a

dominant role of the exchange rate channel in Zambia’s MTM, with lesser roles for the

interest and credit channels.5

While there is evidence to suggest that interest rates are gaining in importance compared to

monetary aggregates in Zambia’s MTM, especially with regards to inflation, it still appears

too early to abandon the traditional policy focus on monetary aggregates. While interest rates

appear to have gained in importance, based on available data one cannot conclude that

interest rates have become more important towards influencing macroeconomic outcomes

than monetary aggregates. A key implication from this review of the empirical evidence is

therefore that monetary policy in Zambia should continue to consider developments in

5 The presence of the expectations and asset price channels are harder to assess given limited data availability,

but these are unlikely to be that important in Zambia considering the relatively small size and basic nature of the

country’s financial sector.

23

monetary aggregates while gradually transitioning to inflation targeting. At the same time,

efforts to enhance the MTM, by promoting financial deepening and economic development

more generally, would assist in ensuring that monetary policy—through monetary aggregates

or via the policy rate—can continue to be effective in securing the overriding objective of

macroeconomic stability.

24

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Taylor, J. B., 1993, ―Discretion versus Policy Rules in Practice,‖ Carnegie Rochester

Conference Series on Public Policy, 39, pp. 195–214.

Taylor, J. B., 1995, ―The Monetary Transmission Mechanism: An Empirical Framework,‖

Journal of Economic Perspectives, Vol.9, No.4, pp.11-26.

29

APPENDIX 1:

The study used the following data.

Description Frequency Source Span

Policy rate

Bank of Zambia policy

rate, in percent

Monthly Bank of

Zambia

April 2012 –

October 2013

T-bill rate Monthly average of 91-

day Treasury Bill yield,

in percent

Monthly Bank of

Zambia

January 1994-

October 2013

Interbank rate Monthly average

overnight interest rate at

which commercial

banks lend to each other

in the money market.

Monthly Bank of

Zambia

January 1994-

October 2013

Deposit rate

Monthly weighted

average of interest rates

on commercial bank

deposits.

Monthly Bank of

Zambia

January 1994-

October 2013

Lending rate

Monthly weighted

average of interest rates

on commercial bank

loans

Monthly Bank of

Zambia

January 1994-

October 2013

Reserve money Monthly average of total

liquid assets of

commercial banks plus

currency in circulation.

Monthly Bank of

Zambia

January 1994-

October 2013

Broad money Monthly average of

(M3) money supply

including foreign

currency deposits.

Monthly Bank of

Zambia

January 1994-

October 2013

Consumer prices

Consumer price index. Monthly Central

Statistical

Office

January 1994-

October 2013

Nominal effective

exchange rate

Monthly average

Kwacha/US dollar

exchange rate.

Monthly Bank of

Zambia

January 1994-

October 2013

Credit to the

private sector

Total commercial bank

credit to the private

sector.

Monthly Bank of

Zambia

January 1994-

October 2013

Electricity

production

Total production of

electricity in kWh .

Monthly ZESCO January 1994-

October 2013

30

Industrial

production

Index of industrial

production.

Quarterly Central

Statistical

Office

2001Q1-

2013Q2

Federal Funds

rate

US Federal Funds Rate. Monthly Federal

Reserve Bank

of New York

Website

January 1994-

October 2013

Oil price US dollar price of crude

oil on the international

market.

Monthly IMF January 1994-

October 2013

Commodity price Index of world

commodity prices.

Monthly IMF January 1994-

October 2013

31

APPENDIX 2

Detailed estimation results behind Table 2.

Table A1: Lending Rates and Interbank Rate Estimations, Entire Sample Period

Dependent Variable: D(LR)

Method: Least Squares

Sample (adjusted): 1995M04 2013M10

Included observations: 223 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

D(LR(-1)) 0.337854 0.065645 5.146715 0.0000

D(LR(-2)) 0.107462 0.065492 1.640839 0.1023

D(IBR) 0.016010 0.011059 1.447650 0.1492

D(IBR(-1)) 0.025599 0.012479 2.051421 0.0414

D(IBR(-2)) 0.031131 0.011041 2.819574 0.0053

R-squared 0.201289 Mean dependent var -0.153687

Adjusted R-squared 0.186633 S.D. dependent var 1.303117

S.E. of regression 1.175240 Akaike info criterion 3.182988

Sum squared resid 301.0993 Schwarz criterion 3.259382

Log likelihood -349.9032 Hannan-Quinn criter. 3.213828

Durbin-Watson stat 2.038584

Table A2: Lending Rates and Interbank Rate Estimations, High Interest Rate Volatility

Era


Method: Least Squares

Sample (adjusted): 1995M04 2006M01

Included observations: 130 after adjustments


D(LR(-1)) 0.332858 0.086523 3.847063 0.0002

D(LR(-2)) 0.103943 0.086335 1.203954 0.2309

D(IBR) 0.016925 0.014052 1.204453 0.2307

D(IBR(-1)) 0.026715 0.015984 1.671389 0.0971

D(IBR(-2)) 0.031654 0.014084 2.247509 0.0264







32

Table A3: Lending Rates and Interbank Rate Estimations, Low Interest Rate Volatility

Era


Sample: 2006M02 2013M10

Included observations: 93


D(LR(-1)) 0.380517 0.104752 3.632572 0.0005

D(LR(-2)) 0.134533 0.104473 1.287737 0.2012

D(IBR) -0.013500 0.039666 -0.340343 0.7344

D(IBR(-1)) 0.011658 0.032003 0.364265 0.7165

D(IBR(-2)) 0.019300 0.030885 0.624896 0.5337







33

APPENDIX 3

Detailed estimation results behind Tables 3 and 4.

Table A4: Pairwise Granger Causality with Policy Rate, 2012M4-2013M9

Lags: 2 Null Hypothesis: Obs F-Statistic Prob. TB91 does not Granger Cause POLICYR 19 0.85324 0.4470

POLICYR does not Granger Cause TB91 2.86591 0.0905 INTERBANKR does not Granger Cause POLICYR 19 3.36170 0.0642

POLICYR does not Granger Cause INTERBANKR 0.10697 0.8993 LENDINGR does not Granger Cause POLICYR 19 0.52247 0.6042

POLICYR does not Granger Cause LENDINGR 1.17497 0.3375 DEPR does not Granger Cause POLICYR 19 3.58998 0.0551

POLICYR does not Granger Cause DEPR 0.02850 0.9720 INTERBANKR does not Granger Cause TB91 19 0.50126 0.6162

TB91 does not Granger Cause INTERBANKR 5.39090 0.0184 LENDINGR does not Granger Cause TB91 19 0.73673 0.4963

TB91 does not Granger Cause LENDINGR 0.04928 0.9521 DEPR does not Granger Cause TB91 19 0.57896 0.5734

TB91 does not Granger Cause DEPR 2.46116 0.1214 LENDINGR does not Granger Cause INTERBANKR 19 2.34996 0.1318

INTERBANKR does not Granger Cause LENDINGR 1.59251 0.2381 DEPR does not Granger Cause INTERBANKR 19 8.67888 0.0035

INTERBANKR does not Granger Cause DEPR 1.00094 0.3924 DEPR does not Granger Cause LENDINGR 19 0.65180 0.5362

LENDINGR does not Granger Cause DEPR 0.33832 0.7186

Table A5: Granger Causality Tests, 1995M1-2013M9

Lags: 2 Null Hypothesis: Obs F-Statistic Prob. INTERBANKR does not Granger Cause TB91 200 1.83770 0.1619

TB91 does not Granger Cause INTERBANKR 7.87630 0.0005 LENDINGR does not Granger Cause TB91 200 0.20735 0.8129

TB91 does not Granger Cause LENDINGR 16.9509 2.E-07 DEPR does not Granger Cause TB91 200 1.03844 0.3560

TB91 does not Granger Cause DEPR 20.2004 1.E-08 LM0 does not Granger Cause TB91 200 2.13922 0.1205

TB91 does not Granger Cause LM0 0.00335 0.9967

34

LM3 does not Granger Cause TB91 200 2.44641 0.0893

TB91 does not Granger Cause LM3 1.54630 0.2156 D12LCPI does not Granger Cause TB91 200 4.43377 0.0131

TB91 does not Granger Cause D12LCPI 0.22374 0.7997 LNEER does not Granger Cause TB91 200 0.74370 0.4767

TB91 does not Granger Cause LNEER 3.48584 0.0326 LCREDIT does not Granger Cause TB91 200 2.66062 0.0724

TB91 does not Granger Cause LCREDIT 3.14474 0.0453 LELECTRICITYG does not Granger Cause TB91 200 1.17989 0.3095

TB91 does not Granger Cause LELECTRICITYG 0.97604 0.3786 LENDINGR does not Granger Cause INTERBANKR 200 4.62804 0.0109

INTERBANKR does not Granger Cause LENDINGR 4.66070 0.0105 DEPR does not Granger Cause INTERBANKR 200 2.22152 0.1112

INTERBANKR does not Granger Cause DEPR 9.08061 0.0002 LM0 does not Granger Cause INTERBANKR 200 6.00062 0.0030

INTERBANKR does not Granger Cause LM0 1.63258 0.1981 LM3 does not Granger Cause INTERBANKR 200 6.75087 0.0015

INTERBANKR does not Granger Cause LM3 0.88630 0.4138 D12LCPI does not Granger Cause INTERBANKR 200 18.1865 6.E-08

INTERBANKR does not Granger Cause D12LCPI 0.10245 0.9027 LNEER does not Granger Cause INTERBANKR 200 6.96489 0.0012

INTERBANKR does not Granger Cause LNEER 5.42582 0.0051 LCREDIT does not Granger Cause INTERBANKR 200 5.63106 0.0042

INTERBANKR does not Granger Cause LCREDIT 5.36935 0.0054 LELECTRICITYG does not Granger Cause INTERBANKR 200 4.34134 0.0143

INTERBANKR does not Granger Cause LELECTRICITYG 0.97724 0.3782 DEPR does not Granger Cause LENDINGR 200 5.81248 0.0035

LENDINGR does not Granger Cause DEPR 8.71049 0.0002 LM0 does not Granger Cause LENDINGR 200 1.40454 0.2480

LENDINGR does not Granger Cause LM0 1.71107 0.1834 LM3 does not Granger Cause LENDINGR 200 1.61511 0.2015

LENDINGR does not Granger Cause LM3 0.30946 0.7342 D12LCPI does not Granger Cause LENDINGR 200 13.5274 3.E-06

LENDINGR does not Granger Cause D12LCPI 0.10460 0.9007 LNEER does not Granger Cause LENDINGR 200 0.11923 0.8877

LENDINGR does not Granger Cause LNEER 0.94607 0.3900 LCREDIT does not Granger Cause LENDINGR 200 2.38002 0.0952

LENDINGR does not Granger Cause LCREDIT 7.07877 0.0011 LELECTRICITYG does not Granger Cause LENDINGR 200 0.34649 0.7076

LENDINGR does not Granger Cause LELECTRICITYG 1.73800 0.1786 LM0 does not Granger Cause DEPR 200 1.97469 0.1416

DEPR does not Granger Cause LM0 0.32438 0.7234

35

LM3 does not Granger Cause DEPR 200 2.22851 0.1104

DEPR does not Granger Cause LM3 0.11840 0.8884 D12LCPI does not Granger Cause DEPR 200 8.33866 0.0003

DEPR does not Granger Cause D12LCPI 0.92552 0.3981 LNEER does not Granger Cause DEPR 200 0.38329 0.6821

DEPR does not Granger Cause LNEER 0.86006 0.4247 LCREDIT does not Granger Cause DEPR 200 5.15130 0.0066

DEPR does not Granger Cause LCREDIT 10.2099 6.E-05 LELECTRICITYG does not Granger Cause DEPR 200 0.47772 0.6209

DEPR does not Granger Cause LELECTRICITYG 1.72442 0.1810 LM3 does not Granger Cause LM0 200 15.1341 8.E-07

LM0 does not Granger Cause LM3 0.39356 0.6752 D12LCPI does not Granger Cause LM0 200 0.51302 0.5995

LM0 does not Granger Cause D12LCPI 4.40372 0.0135 LNEER does not Granger Cause LM0 200 1.37782 0.2546

LM0 does not Granger Cause LNEER 1.97352 0.1417 LCREDIT does not Granger Cause LM0 200 1.41836 0.2446

LM0 does not Granger Cause LCREDIT 1.42810 0.2423 LELECTRICITYG does not Granger Cause LM0 200 2.87420 0.0589

LM0 does not Granger Cause LELECTRICITYG 3.03905 0.0502 D12LCPI does not Granger Cause LM3 200 1.81721 0.1652

LM3 does not Granger Cause D12LCPI 6.37195 0.0021 LNEER does not Granger Cause LM3 200 0.36440 0.6951

LM3 does not Granger Cause LNEER 2.64480 0.0736 LCREDIT does not Granger Cause LM3 200 0.81167 0.4456

LM3 does not Granger Cause LCREDIT 2.54533 0.0811 LELECTRICITYG does not Granger Cause LM3 200 1.36814 0.2570

LM3 does not Granger Cause LELECTRICITYG 8.97522 0.0002 LNEER does not Granger Cause D12LCPI 200 4.63597 0.0108

D12LCPI does not Granger Cause LNEER 3.57211 0.0299 LCREDIT does not Granger Cause D12LCPI 200 5.52004 0.0047

D12LCPI does not Granger Cause LCREDIT 2.65899 0.0726 LELECTRICITYG does not Granger Cause D12LCPI 200 3.05637 0.0493

D12LCPI does not Granger Cause LELECTRICITYG 1.08788 0.3390 LCREDIT does not Granger Cause LNEER 200 1.57634 0.2094

LNEER does not Granger Cause LCREDIT 0.60151 0.5490 LELECTRICITYG does not Granger Cause LNEER 200 1.49458 0.2269

LNEER does not Granger Cause LELECTRICITYG 1.91825 0.1496 LELECTRICITYG does not Granger Cause LCREDIT 200 0.80622 0.4480

LCREDIT does not Granger Cause LELECTRICITYG 4.65824 0.0106

36

APPENDIX 4

Figure A1: Model 2-Impulse Response

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35


-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35


-.008

-.004

.000

.004

.008

5 10 15 20 25 30 35


-.008

-.004

.000

.004

.008

5 10 15 20 25 30 35


-.02

.00

.02

.04

.06

5 10 15 20 25 30 35


-.02

.00

.02

.04

.06

5 10 15 20 25 30 35


-2

0

2

4

6

8

5 10 15 20 25 30 35


-2

0

2

4

6

8

5 10 15 20 25 30 35


-.02

-.01

.00

.01

.02

5 10 15 20 25 30 35


-.02

-.01

.00

.01

.02

5 10 15 20 25 30 35



37

Table A6: Model 2-Variance Decomposition

Variance Decomposition of LELECTRICITYG:



R LNEER 1 0.065838 100.0000 0.000000 0.000000 0.000000 0.000000

6 0.106766 95.37550 1.586901 1.665285 1.122957 0.249360 12 0.111482 93.75026 1.484071 1.750769 2.495974 0.518930 24 0.111859 93.26764 1.528887 1.747562 2.674353 0.781558 36 0.111886 93.22426 1.551616 1.747402 2.673858 0.802869




R LNEER 1 0.010273 2.182492 97.81751 0.000000 0.000000 0.000000

6 0.031235 8.845017 83.85221 1.840590 0.765237 4.696945 12 0.043176 9.149851 82.88137 2.479994 1.844373 3.644407 24 0.058750 7.352357 84.42271 2.613020 2.797673 2.814238 36 0.070068 6.577456 85.31866 2.597971 3.071335 2.434577




R LNEER 1 0.051764 3.579816 0.461511 95.95867 0.000000 0.000000

6 0.083497 27.95309 1.580535 65.14438 3.078041 2.243956 12 0.095676 38.47934 3.217174 52.39854 2.841492 3.063451 24 0.106063 32.97108 17.42498 43.80502 2.466062 3.332866 36 0.117061 27.59987 30.37561 36.51068 2.504209 3.009622




R LNEER 1 6.139427 2.427775 0.122274 0.161415 97.28854 0.000000

6 7.591180 2.092713 9.181387 0.463995 86.44598 1.815923 12 7.648964 2.249487 9.055498 0.505905 85.90830 2.280810 24 7.656275 2.250872 9.043050 0.508756 85.74873 2.448592 36 7.657535 2.251128 9.057789 0.511620 85.72113 2.458333




R LNEER 1 0.038568 0.850065 1.949324 0.000642 0.154880 97.04509

6 0.089753 7.082571 1.523902 0.217908 3.106110 88.06951 12 0.100709 7.829391 1.223303 1.111199 2.670841 87.16526 24 0.104390 7.419784 1.838048 1.855521 2.539219 86.34743 36 0.105983 7.226656 3.978741 2.023404 2.512895 84.25830

38

Varian

ce Decompositio

n of LELECTRICIT

YG:



R LNEER 1 0.065838 100.0000 0.000000 0.000000 0.000000 0.000000

2 0.077955 98.22581 0.000151 0.900692 0.733174 0.140170 3 0.092096 97.29975 0.833300 1.201505 0.528895 0.136548 4 0.098800 96.39196 1.352841 1.386524 0.743687 0.124990 5 0.103850 96.01205 1.501022 1.579924 0.731402 0.175606 6 0.106766 95.37550 1.586901 1.665285 1.122957 0.249360 7 0.108678 95.06579 1.546634 1.710644 1.361845 0.315089 8 0.109854 94.62357 1.526527 1.732184 1.758637 0.359080 9 0.110590 94.33276 1.506698 1.742027 2.020587 0.397927

10 0.111035 94.07707 1.495266 1.747703 2.244757 0.435208 11 0.111311 93.89408 1.487925 1.749879 2.391804 0.476314 12 0.111482 93.75026 1.484071 1.750769 2.495974 0.518930 13 0.111593 93.64010 1.483341 1.750610 2.565014 0.560939 14 0.111667 93.55424 1.485183 1.750131 2.610708 0.599739 15 0.111718 93.48810 1.488718 1.749572 2.639359 0.634252 16 0.111753 93.43682 1.493224 1.749071 2.656634 0.664254 17 0.111780 93.39689 1.498151 1.748664 2.666306 0.689988 18 0.111800 93.36527 1.503170 1.748343 2.671418 0.711800 19 0.111815 93.33989 1.508130 1.748096 2.673826 0.730059 20 0.111828 93.31928 1.512891 1.747909 2.674786 0.745135 21 0.111838 93.30242 1.517392 1.747771 2.674996 0.757426 22 0.111846 93.28855 1.521568 1.747673 2.674868 0.767339 23 0.111853 93.27711 1.525401 1.747606 2.674616 0.775264 24 0.111859 93.26764 1.528887 1.747562 2.674353 0.781558 25 0.111863 93.25977 1.532042 1.747535 2.674126 0.786528 26 0.111867 93.25321 1.534887 1.747519 2.673952 0.790433 27 0.111871 93.24773 1.537448 1.747509 2.673829 0.793487 28 0.111874 93.24313 1.539752 1.747501 2.673752 0.795866 29 0.111876 93.23926 1.541822 1.747494 2.673710 0.797711 30 0.111878 93.23600 1.543685 1.747486 2.673696 0.799138 31 0.111880 93.23322 1.545361 1.747476 2.673701 0.800237 32 0.111881 93.23086 1.546872 1.747464 2.673720 0.801080 33 0.111883 93.22884 1.548237 1.747451 2.673749 0.801725 34 0.111884 93.22709 1.549473 1.747436 2.673783 0.802216 35 0.111885 93.22558 1.550595 1.747419 2.673820 0.802588 36 0.111886 93.22426 1.551616 1.747402 2.673858 0.802869

Varian

ce Decompositio

n of LCPI:



R LNEER 1 0.010273 2.182492 97.81751 0.000000 0.000000 0.000000

2 0.016460 4.376240 93.72254 0.240243 0.403406 1.257576 3 0.021590 5.535173 89.44129 1.019561 1.107971 2.896001 4 0.025646 6.858251 86.88916 1.341634 0.817464 4.093490 5 0.028622 8.073755 84.96283 1.624543 0.663475 4.675399 6 0.031235 8.845017 83.85221 1.840590 0.765237 4.696945 7 0.033552 9.340385 83.17597 2.011319 0.949274 4.523049 8 0.035735 9.524377 82.87313 2.154826 1.157393 4.290277 9 0.037785 9.544320 82.75760 2.268088 1.349109 4.080885

10 0.039700 9.458079 82.75287 2.359355 1.520845 3.908848 11 0.041494 9.317841 82.80048 2.428176 1.687207 3.766293

Figure A2: Model 3-Impulse Responses

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35


-.01

.00

.01

.02

.03

5 10 15 20 25 30 35


-.008

-.004

.000

.004

.008

.012

5 10 15 20 25 30 35


-.008

-.004

.000

.004

.008

.012

5 10 15 20 25 30 35


-.01

.00

.01

.02

.03

.04

5 10 15 20 25 30 35


-.01

.00

.01

.02

.03

.04

5 10 15 20 25 30 35


-2

0

2

4

6

8

5 10 15 20 25 30 35


-2

0

2

4

6

8

5 10 15 20 25 30 35


-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35

Response of LCREDIT to LM3

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35

Response of LCREDIT to INTERBANKR

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35


-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35



39





R LCREDIT LNEER 1 0.063668 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000

12 0.102840 90.00095 1.233696 3.036357 2.686366 2.606213 0.436414 24 0.104465 87.45839 1.891811 2.963742 2.637146 4.374780 0.674132 36 0.104926 86.71333 2.067232 2.946649 2.614179 4.805109 0.853502




R LCREDIT LNEER 1 0.010229 2.985471 97.01453 0.000000 0.000000 0.000000 0.000000

12 0.041989 12.59992 71.85664 12.28289 2.479145 0.724951 0.056453 24 0.057176 8.085301 67.49934 20.22998 2.903362 1.246314 0.035698 36 0.068202 5.945797 65.70682 24.20480 2.929889 1.187285 0.025412




R LCREDIT LNEER 1 0.035379 0.060887 0.312519 99.62659 0.000000 0.000000 0.000000

12 0.079255 20.23934 3.130735 74.18332 0.533857 1.705376 0.207369 24 0.087744 19.23896 6.405034 69.34201 0.651213 3.862212 0.500574 36 0.094157 16.71729 11.74576 65.60569 1.012393 4.043797 0.875070




R LCREDIT LNEER 1 6.199045 3.598411 0.000212 0.141802 96.25957 0.000000 0.000000

12 7.218353 4.510600 5.769138 5.474883 83.54386 0.063325 0.638192 24 7.294285 5.466997 6.227668 5.470454 81.84549 0.289707 0.699687 36 7.312868 5.508069 6.381936 5.457550 81.43705 0.482003 0.733391

Variance Decomposition of LCREDIT:



R LCREDIT LNEER 1 0.044118 0.003896 2.275127 11.98183 0.249993 85.48916 0.000000

12 0.094317 0.363528 4.357148 17.42004 1.569477 70.45658 5.833227 24 0.109463 0.722504 5.921535 20.25419 1.813760 62.40246 8.885549 36 0.120062 0.630955 10.88319 23.14046 2.055305 54.34060 8.949486




R LCREDIT LNEER 1 0.037635 0.725985 0.943117 17.35665 0.171309 1.030492 79.77244

12 0.093392 5.405879 2.352226 22.37297 3.778363 1.999686 64.09088 24 0.099375 5.075162 5.892687 22.18473 3.610213 4.354132 58.88308 36 0.102446 4.900054 8.672392 22.07453 3.497746 5.083608 55.77167

40


-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35


-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35


-.012

-.008

-.004

.000

.004

.008

5 10 15 20 25 30 35


-.012

-.008

-.004

.000

.004

.008

5 10 15 20 25 30 35


-.02

.00

.02

.04

.06

5 10 15 20 25 30 35


-.02

.00

.02

.04

.06

5 10 15 20 25 30 35


-2

0

2

4

6

8

5 10 15 20 25 30 35


-2

0

2

4

6

8

5 10 15 20 25 30 35


-.015

-.010

-.005

.000

.005

.010

5 10 15 20 25 30 35


-.015

-.010

-.005

.000

.005

.010

5 10 15 20 25 30 35


-.03

-.02

-.01

.00

.01

.02

5 10 15 20 25 30 35


-.03

-.02

-.01

.00

.01

.02

5 10 15 20 25 30 35



41





R LCREDIT LNEER 1 0.064372 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000

12 0.106888 87.86076 4.145198 0.897948 3.116831 3.738909 0.240358

24 0.108279 85.73080 5.018709 0.878524 3.316413 4.723910 0.331648

36 0.108691 85.08643 5.229278 0.876956 3.319644 5.091651 0.396041 Variance Decomposition of LCPI:



R LCREDIT LNEER 1 0.010076 1.415596 98.58440 0.000000 0.000000 0.000000 0.000000

12 0.042303 7.640716 82.64464 1.724736 5.230246 0.587167 2.172499

24 0.059439 5.821457 83.32036 1.629383 7.297183 0.350751 1.580863

36 0.072262 5.203844 83.63174 1.579125 7.960814 0.270613 1.353866 Variance Decomposition of LM0:



R LCREDIT LNEER 1 0.051979 4.006111 0.338825 95.65506 0.000000 0.000000 0.000000

12 0.096554 38.43356 2.673692 52.05427 2.891471 2.013972 1.933037

24 0.105292 33.56149 13.57404 45.09824 2.831387 2.656154 2.278691

36 0.115553 28.26048 25.69776 37.84067 3.691704 2.432557 2.076832 Variance Decomposition of INTERBANKR:



R LCREDIT LNEER 1 6.132121 2.833814 0.006092 0.246265 96.91383 0.000000 0.000000

12 7.483428 3.427567 9.463756 0.626266 83.37109 2.060269 1.051048

24 7.494481 3.424403 9.524393 0.627380 83.12898 2.170704 1.124137

36 7.499448 3.419965 9.550414 0.628932 83.02151 2.237656 1.141522 Variance Decomposition of LCREDIT:



R LCREDIT LNEER 1 0.042195 0.170913 2.096540 0.087613 0.193065 97.45187 0.000000

12 0.088172 0.426880 1.964297 0.048618 0.257528 96.32677 0.975905

24 0.105619 1.555222 8.814889 0.091965 1.168058 86.62964 1.740223

36 0.119595 2.551661 19.06966 0.175363 2.501684 74.04335 1.658280 Variance Decomposition of LNEER:



R LCREDIT LNEER 1 0.038177 0.878195 2.302585 0.032691 0.190511 11.22919 85.36683

12 0.092804 3.589143 2.441774 0.453706 3.456765 13.48391 76.57470

24 0.098044 3.218453 7.778689 0.976777 3.419000 12.71720 71.88988

36 0.101922 3.017768 12.74537 1.129242 3.611641 12.38706 67.10892

42


-.01

.00

.01

.02

.03

5 10 15 20 25 30 35


-.01

.00

.01

.02

.03

5 10 15 20 25 30 35


-.01

.00

.01

.02

.03

5 10 15 20 25 30 35

Response of LELECTRICITYG to LENDINGR

-.008

-.004

.000

.004

.008

.012

5 10 15 20 25 30 35


-.008

-.004

.000

.004

.008

.012

5 10 15 20 25 30 35


-.008

-.004

.000

.004

.008

.012

5 10 15 20 25 30 35

Response of LCPI to LENDINGR

-.02

-.01

.00

.01

.02

.03

.04

5 10 15 20 25 30 35


-.02

-.01

.00

.01

.02

.03

.04

5 10 15 20 25 30 35


-.02

-.01

.00

.01

.02

.03

.04

5 10 15 20 25 30 35

Response of LM3 to LENDINGR

-2

0

2

4

6

8

5 10 15 20 25 30 35


-2

0

2

4

6

8

5 10 15 20 25 30 35


-2

0

2

4

6

8

5 10 15 20 25 30 35

Response of INTERBANKR to LENDINGR

-0.4

0.0

0.4

0.8

1.2

1.6

5 10 15 20 25 30 35

Response of LENDINGR to LM3

-0.4

0.0

0.4

0.8

1.2

1.6

5 10 15 20 25 30 35

Response of LENDINGR to INTERBANKR

-0.4

0.0

0.4

0.8

1.2

1.6

5 10 15 20 25 30 35

Response of LENDINGR to LENDINGR

-.03

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35


-.03

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35


-.03

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35

Response of LCREDIT to LENDINGR

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35


-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35


-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35

Response of LNEER to LENDINGR


43



Period S.E. LELECTRICITYG LCPI LM3 INTERBANK LENDINGR LCREDIT LNEER 1 0.063711 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

12 0.103626 87.96071 0.846682 3.510709 2.493506 1.152527 3.366253 0.669616 24 0.105436 85.27063 1.668271 3.452482 2.522163 1.483565 4.514494 1.088392 36 0.106126 84.20201 1.990547 3.481658 2.493350 1.852216 4.519997 1.460218


Period S.E. LELECTRICITYG LCPI LM3 INTERBANK LENDINGR LCREDIT LNEER 1 0.010222 2.906689 97.09331 0.000000 0.000000 0.000000 0.000000 0.000000

12 0.041688 11.92060 71.46125 11.14731 2.059707 1.158359 2.087408 0.165368 24 0.056233 7.795253 65.85306 17.99588 2.930757 1.694348 3.575342 0.155361 36 0.066403 5.792218 64.31078 21.51845 2.998992 1.586678 3.681309 0.111572


Period S.E. LELECTRICITY LCPI LM3 INTERBANK LENDINGR LCREDIT LNEER 1 0.035402 0.115754 0.326668 99.55758 0.000000 0.000000 0.000000 0.000000

12 0.078523 22.02056 3.537140 71.46321 0.430019 1.556548 0.781473 0.211058 24 0.085514 21.90609 5.886052 66.89420 0.542423 2.710657 1.211214 0.849354 36 0.090546 19.55601 9.699240 62.95236 0.978919 4.115187 1.148017 1.550270



12 7.246456 4.291544 5.868128 5.494850 83.48290 0.148563 0.039389 0.674630 24 7.322204 5.263072 6.390036 5.456660 81.78968 0.159341 0.173023 0.768193 36 7.341111 5.313268 6.588120 5.469305 81.37864 0.179786 0.260283 0.810595

Variance Decomposition of LENDINGR:


12 3.583920 3.017202 8.787854 0.677376 4.414551 77.54058 1.822737 3.739704 24 3.664095 2.951975 8.621733 0.685851 4.749248 76.32290 2.695478 3.972819 36 3.674143 2.945524 8.915851 0.751944 4.729901 75.92115 2.742246 3.993384

Variance Decomposition of LCREDIT:


12 0.088008 0.464687 8.236450 11.21258 0.972612 20.40611 46.28008 12.42747 24 0.103560 1.234029 6.461705 9.715930 1.904318 28.42565 33.78411 18.47426 36 0.109317 1.256576 8.416763 10.60117 2.274419 28.61008 30.37337 18.46762



12 0.092986 5.245591 2.667503 20.57474 4.049148 3.862605 1.854563 61.74586 24 0.099470 4.964324 7.257297 20.70110 3.910843 3.645999 3.044487 56.47595 36 0.103096 4.797367 10.64690 20.96312 3.716871 3.582567 3.202655 53.09052

44


-.008

-.004

.000

.004

.008

.012

1 2 3 4 5 6 7 8 9 10 11 12

Response of LIIP to LM3

-.008

-.004

.000

.004

.008

.012

1 2 3 4 5 6 7 8 9 10 11 12

Response of LIIP to INTERBANKR

-.004

.000

.004

.008

.012

.016

1 2 3 4 5 6 7 8 9 10 11 12


-.004

.000

.004

.008

.012

.016

1 2 3 4 5 6 7 8 9 10 11 12


-.02

.00

.02

.04

.06

.08

1 2 3 4 5 6 7 8 9 10 11 12


-.02

.00

.02

.04

.06

.08

1 2 3 4 5 6 7 8 9 10 11 12


-2

0

2

4

6

1 2 3 4 5 6 7 8 9 10 11 12


-2

0

2

4

6

1 2 3 4 5 6 7 8 9 10 11 12


-.04

-.02

.00

.02

.04

1 2 3 4 5 6 7 8 9 10 11 12


-.04

-.02

.00

.02

.04

1 2 3 4 5 6 7 8 9 10 11 12



45


Variance Decomposition of LIIP:

Period S.E. LIIP LCPI LM3 INTERBANK LNEER 1 0.030223 100.0000 0.000000 0.000000 0.000000 0.000000

2 0.032417 97.94881 0.537716 1.056736 0.279472 0.177264 4 0.033813 90.70843 2.460562 5.052804 0.694639 1.083567 8 0.036357 79.15002 6.145453 10.47808 1.151846 3.074601

12 0.038270 72.14648 8.706446 13.21633 1.451757 4.478986 Variance Decomposition of LCPI:

Period S.E. LIIP LCPI LM3 INTERBANK LNEER 1 0.016132 7.704930 92.29507 0.000000 0.000000 0.000000

2 0.024260 22.47478 75.22263 1.813450 0.451113 0.038030 4 0.035135 33.38500 59.32010 6.359173 0.674898 0.260834 8 0.045808 33.96631 50.98780 12.92985 0.932554 1.183490

12 0.051455 31.35929 48.12663 16.86560 1.224887 2.423598 Variance Decomposition of LM3:

Period S.E. LIIP LCPI LM3 INTERBANK

R LNEER 1 0.062962 0.807199 11.49054 87.70226 0.000000 0.000000

2 0.077631 2.424628 10.31911 86.90776 0.000652 0.347853 4 0.090101 5.817103 8.094749 83.96447 0.159197 1.964481 8 0.099997 5.897622 7.869938 79.57771 0.873017 5.781707

12 0.105853 5.315222 9.473446 75.70593 1.385673 8.119723 Variance Decomposition of INTERBANKR:


R LNEER 1 3.878656 3.142882 0.509665 0.106607 96.24085 0.000000

2 4.277638 10.12765 1.750902 4.526980 82.91268 0.681792 4 4.463651 10.37427 4.725123 6.826352 76.28626 1.787998 8 4.660149 14.77753 6.260820 6.403762 70.02386 2.534030

12 4.711434 15.89258 6.600407 6.337924 68.51657 2.652520 Variance Decomposition of LNEER:


R LNEER 1 0.059333 5.477425 1.406491 13.40050 1.694152 78.02143

2 0.078745 12.71528 0.938251 14.81570 3.034847 68.49593 4 0.095729 19.09332 0.734699 15.35068 3.770202 61.05109 8 0.105925 23.96220 1.756551 15.66269 3.619439 54.99912

12 0.109182 25.43968 2.947095 16.09229 3.422189 52.09875

46

Figure A6: Model 1 (Early Sub-period)-Impulse Responses

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35


-.01

.00

.01

.02

.03

5 10 15 20 25 30 35


-.010

-.005

.000

.005

.010

.015

5 10 15 20 25 30 35


-.010

-.005

.000

.005

.010

.015

5 10 15 20 25 30 35


-.02

-.01

.00

.01

.02

.03

.04

5 10 15 20 25 30 35


-.02

-.01

.00

.01

.02

.03

.04

5 10 15 20 25 30 35


-4

0

4

8

12

5 10 15 20 25 30 35


-4

0

4

8

12

5 10 15 20 25 30 35


-.04

-.02

.00

.02

.04

5 10 15 20 25 30 35


-.04

-.02

.00

.02

.04

5 10 15 20 25 30 35



47

Table A11: Model 1 (Early Sub-period)-Variance Decomposition




R LNEER 1 0.067389 100.0000 0.000000 0.000000 0.000000 0.000000

6 0.091186 89.14950 0.596761 4.948038 3.842311 1.463394 12 0.091795 88.26864 0.617707 4.923171 4.216745 1.973734 24 0.091905 88.17544 0.646213 4.973148 4.215692 1.989511 36 0.091951 88.10836 0.681738 5.008754 4.212434 1.988716




R LNEER 1 0.012997 6.627666 93.37233 0.000000 0.000000 0.000000

6 0.036453 21.50396 58.93311 17.06117 0.499019 2.002742 12 0.047845 21.10092 48.69218 28.47831 0.441715 1.286875 24 0.061833 15.88347 44.24913 38.40068 0.614461 0.852251 36 0.072292 14.26663 42.45834 41.85690 0.667821 0.750313




R LNEER 1 0.033033 0.016576 1.689973 98.29345 0.000000 0.000000

6 0.057927 1.229646 5.193407 84.49278 6.611557 2.472612 12 0.064688 2.372226 10.26998 75.88407 7.488587 3.985134 24 0.077017 8.232901 17.70496 64.65733 5.619300 3.785513 36 0.085492 8.737074 21.45855 61.90978 4.741235 3.153354




R LNEER 1 8.256222 3.318481 0.011402 1.210666 95.45945 0.000000

6 10.31929 5.638300 4.189172 5.867300 82.46691 1.838316 12 10.60529 6.757267 4.610646 5.624558 78.40160 4.605929 24 10.80790 9.174853 4.885468 5.522236 75.52749 4.889957 36 10.84213 9.187662 5.093221 5.796370 75.06177 4.860975




R LNEER 1 0.033016 6.785622 0.051006 8.183236 0.432430 84.54771

6 0.103433 23.29695 4.905508 9.559549 6.725378 55.51261 12 0.120284 33.57374 4.407003 7.463301 7.554147 47.00181 24 0.125281 36.31227 5.247732 7.198647 7.283403 43.95795 36 0.126408 35.91944 5.821188 7.890113 7.182047 43.18721

48

Figure A7: Model 1 (Late Sub-period)-Impulse Responses

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35


-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35


-.004

-.002

.000

.002

.004

.006

5 10 15 20 25 30 35


-.004

-.002

.000

.002

.004

.006

5 10 15 20 25 30 35


-.01

.00

.01

.02

.03

.04

.05

5 10 15 20 25 30 35


-.01

.00

.01

.02

.03

.04

.05

5 10 15 20 25 30 35


-0.5

0.0

0.5

1.0

1.5

2.0

2.5

5 10 15 20 25 30 35


-0.5

0.0

0.5

1.0

1.5

2.0

2.5

5 10 15 20 25 30 35


-.03

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35


-.03

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30 35



49

Table A12: Model 1 (Late Sub-period)-Variance Decomposition




R LNEER 1 0.053413 100.0000 0.000000 0.000000 0.000000 0.000000

6 0.064722 80.78515 3.166141 11.52032 2.206490 2.321902 12 0.067719 74.88460 6.204323 11.99993 4.087288 2.823854 24 0.068379 73.49546 6.902888 12.51177 4.176754 2.913130 36 0.068831 72.55685 7.257348 12.77234 4.197256 3.216205




R LNEER 1 0.004626 0.695063 99.30494 0.000000 0.000000 0.000000

6 0.012630 1.065435 68.29798 25.18740 3.187178 2.262011 12 0.016775 2.170026 54.70273 33.89597 2.122552 7.108717 24 0.022182 2.109474 45.19374 33.09918 4.087012 15.51059 36 0.025943 2.031403 39.61957 32.26722 6.704868 19.37694




R LNEER 1 0.037058 0.002104 0.106649 99.89125 0.000000 0.000000

6 0.057507 8.861625 1.269961 81.06279 6.249064 2.556561 12 0.066456 7.731988 6.925594 66.51803 8.759854 10.06454 24 0.079263 5.887722 11.73096 55.24283 10.82226 16.31623 36 0.088019 5.106101 13.48256 50.17374 12.13565 19.10194




R LNEER 1 1.779050 3.332834 0.042419 0.658032 95.96672 0.000000

6 2.826802 1.867688 1.761539 11.43881 83.81961 1.112356 12 3.119280 2.358400 2.281098 11.30331 80.99366 3.063525 24 3.247627 2.215931 2.123772 10.87368 79.71311 5.073508 36 3.282186 2.192596 2.162924 10.97871 79.02880 5.636974



TYG LCPI LM3 INTERBANK LNEER 1 0.037790 0.304184 3.609009 20.57147 5.458392 70.05694

6 0.065027 1.450357 2.928089 20.13947 23.02868 52.45341 12 0.070384 1.959881 4.119569 17.82619 28.93300 47.16136 24 0.072679 1.843867 5.851848 16.94342 30.96937 44.39149 36 0.073100 1.840102 6.376911 17.06958 30.73555 43.97786

41_Monetary Policy Transmission Mechanism in Zambia

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Transcript of 41_Monetary Policy Transmission Mechanism in Zambia