Tarun Das ADB Nepal Upgraded Macro Economic Model Part-2

8/9/2019 Tarun Das ADB Nepal Upgraded Macro Economic Model Part-2

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Upgraded Nepal Macroeconomic Model- Test, Calibrations and Projections

UPGRADED NEPAL MACROECONOMIC MODEL

PART-TWO

PROJECTIONS, SIMULATIONS, MODEL RESULTSAND POLICY CONCLUSIONS

DR. TARUN DAS1,

(FORMERLY, ECONOMIC ADVISER, MINISTRYOF FINANCE, INDIA)

PROF. DURGA LAL2 SHRESTHA,

DR. VIKAS RAJ SATYAL3

MR. ROJAN BAJRACHARYA4

Asian Development Bank Project No.TA-7165-NEPStrengthening Capacity for Macroeconomic Analysis (Component B)

Nepal Rastra BankBaluwater, Kathmandu, Nepal

30 May 2010

For any clarifications, write to: [email protected]

1Team Leader and International Macroeconomic Modeling Specialist2

National Macroeconomic Modeling Specialist3 National Econometrician4 National IT Specialist

1
mailto:[email protected]://flagspot.net/images/n/np)2006.gifmailto:[email protected]


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PART-TWO

ANALYTICAL FRAMEWORK, MODEL SPECIFICATIONS, DATA BASETESTS, CALIBRATION TECHNIQUES AND COMPUTER ALGORITHMS

Acknowledgements

The Modeling Team would like to express their sincere gratitude and thanks to:

Mr. Ravindra Prasad Pande, Executive Director (Research), Nepal

Rastra Bank (NRB) and Chairman, Technical Committee on Modeling, for thestrategic guidance on modeling and economic analysis;

Dr. Nephil Matangi Maskay, Director (Research), NRB and FocalOfficer, Technical Committee on Modeling, for overall supervision and day-to-day advice on modeling and research;

Mr. Yubraj Acharya, ADB Project Implementation Officer; Mr. ShahidParwez, former Program/ Project Implementation Officer, Mr. Raju Tuladhar,Senior Economics Officer;.Ms. Shreejana Rajbhandari, Finance Officer andMs. Neelina Nakarmi, ADB Nepal Resident Mission at Kathmandu forproviding all possible support for the project;

Dr. Ram Sharan Kharel, Deputy Director (Research) and Secretary,Technical Committee for valuable discussions and comments on modeling;

Mr. Suman Neupane, Assistant Director (Research), NRB for logisticssupport and academic help on data base and methodology; and

All the Directors and experts of the concerned divisions in theResearch Department, NRB for their enthusiastic cooperation, keen interest,useful discussions and for providing relevant data and documents.

Dr. Tarun Das, Team LeaderAnd International Macroeconomic Modeling Specialist

30 May 2010

Kathmandu, Nepal

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UPGRADED NEPAL MACROECONOMIC MODEL-

PART-TWO

ANALYTICAL FRAMEWORK, MODEL SPECIFICATIONS, DATA BASETESTS, CALIBRATION TECHNIQUES AND COMPUTER ALGORITHMS

CONTENTS

Contents Pages

Acknowledgements 2

Contents 3

1. Analytical Framework and Model Structure 4-10

2. Data Base 10-12

3. Basic Assumptions for projections 12-13

4. Test and Calibration Techniques 13-17

5. Computer Algorithms 18

Annex-1: Data Base-Basic concepts, sources of data

and adjustments

20-23

Annex-2: Description of variables and identities 24-29

Annex-3: Time series data for 1975-2009 30-51

Annex-4: Standard descriptive statistics 52-56

Annex-5: A brief description of standard statistics used for

modeling and test and calibrations

57-64

Annex-6: Summary results of KPSS Unit Trend Stationarity

Tests for the variables

65-68

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PART-TWO

ANALYTICAL FRAMEWORK, MODEL SPECIFICATIONS, DATABASE,TESTS, CALIBRATION TECHNIQUES AND COMPUTER ALGORITHMS

1. Analytical Framework and Model Structure

1.1Purpose and Objectives

The basic purpose of this exercise is to build an operational macroeconomic model

for Nepal in the consistent macroeconomic accounting framework, depicting theunderlying structure, trends and interrelations among major macroeconomic

variables. The detailed analytical framework and structures of sub-models were

described in the Inception Report prepared in October 2009. These have been

modified in the light of written comments provided by the EAD and concerned

divisions of the Research Department, NRB followed by subsequent discussions with

them in the first week of April 2010.

The analytical framework is consistent with the basic concepts and interrelationsunder the UN System of National Accounts (UN-SNA), and the IMF Government

Finance Statistics (GFS), Balance of Payments (BOP) Statistics and the Monetary-Financial Statistics (MFS). Thus, the model consists of five inter-related accounts asindicated below:(i) Real Sector (National Accounts)(ii) Government Sector (Fiscal Account)(iii) External Sector (Balance of Payments Account)(iv) Monetary Sector (Monetary and Financial Account), and(v) Prices, Interest and Exchange Rates.

1.2 Model Structure and Analytical Framework

Basic structures of the sub-models and their interrelations are depicted in the form ofa flow diagram in Box-1.1 and Figure-1.1. The linkages between key aggregates of

the national accounts and the balance of payments flows and government finance

statistics can be summarized algebraically within a savings/investment framework.

Let us use the following symbols,

National Accounts System (NAS)

GDPFC = Real GDP at constant FC = GDPSectorGDPSector = Sector (Agriculture, industry, services) GDP at FCC

= private consumption expenditureG = government consumption expenditure

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I = gross domestic investmentS = gross savingX = exports of goods and (non-factor) servicesM = imports of goods and (non-factor) servicesNFY = net factor income from abroad

GDP = gross domestic productGNP = gross national product = GDP + NFYGNDY = gross national disposable income + Net Transfer

On the supply side, overall GDP equals the sectoral value added for different sectorssuch as agriculture, industry and services. In our model, industry has four sub-sectors viz. mining and quarrying, manufacturing, construction and public utilities(comprising electricity, gas and water supply). Service has four sub-sectors viz. (a)wholesale and retail trade, hotels and restaurants; (b) transport and communications;(c) financial services and real estate; and (d) social sectors comprising health,education, public administration, religious, personal and community services. GDP

for the sub-sectors are projected at both constant factor cost and current marketprices.

On the demand side, GDP at factor cost equals government consumption, privateconsumption, investment and inventories, and exports less imports. These items areprojected at current basic prices. By adding indirect taxes less subsidies, we get theGDP at current market prices. GNI equals GDP plus Net Factor Income from abroad,while Gross National Disposable Income equals GNI plus Net Transfer.

Balance of Payments (BOP)

CAB = current account balance in the balance of payments = TB + SB + FIB + NCTRT = reserve asset transactionsTB = Merchandise trade balance = Merchandise Exports Merchandise ImportsSB = Non-factor service balance (travel, tourism, financial, business, ICT etc.)FIB = Factor income balance (interest, dividends, wages, rent, royalties etc.)NCT = Net current transfer (official grants, private grants, remittances)NKT = net capital transfersNPNNA = net purchases of non-produced, non-financial assetsNFI = net foreign investment or net lending/ net borrowing vis--vis the rest of theworld

NKA = net capital and financial account (i.e., all capital and financial transactionsexcluding reserve assets)

Government Finance Statistics (GFS)

R = Revenue = T + NTT = Tax revenueNT = Non-Tax revenueGR = GrantsGEXP = Government expenditure and net lending = G + GKG = government consumption expenditure

GK = Government capital expenditure and net lending = GCE + NDGCE = Government capital expenditure

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ND = Net lendingGFD = Gross Fiscal Deficit = (R+GR) - GEXP

However, various components of taxes have been considered in the actualcalibrations. These are described in details in the sub-models.

Monetary-Financial Statistics (MFS)

MD = Demand for money = NFA+CRG + CRP + OIMS = Supply of money = DD + TD + CNNFA = Net foreign assetsCRG = Credits to the government and public enterprisesCRP = Credits to the private sectorOI = Other itemsDD = Demand deposits

TD = Time depositsCN = Currency and notes in circulation

Prices, Interest Rates and Exchange Rates

CPI = Consumer Price IndexWPI = Wholesale Price IndexGDPDF = GDP DeflatorINT = Rate of interestPLR = Prime lending rateFDR = Fixed deposit rateER = Exchange rate of Nepalese Rupee per US dollar

Various other prices, deposit rates, lending rates and inflation rates are considered inthe sub-model.

The Macroeconomic Balance Equations stand as follow:

GDP = C + G + I +XM (1)(XM= balance on goods and services in the balance of payments)GNP = GDP + NFY (2)

CAB = X M + NFY+NCT (3)GNDY = C + G + I + CAB (4)GNDY = C + G + S (5)

Equating (4) and (5) we get:S I = CAB i.e. saving-investment gap (resource gap) equals CAB (6)S I + (NKT NPNNA) = CAB + (NKTNPNNA) = NFI (7)(NKT NPNNA) = balance on the capital account of the balance of payments.

Interrelationship between the internal and external sectors of an economy can beseen in greater detail by distinguishing between private and public sectors. Private

saving and investment (Sp and Ip) and government saving and investment (Sg andIg) are identified as:

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SI = Sp+SgIpIg (8)

Use of the definition of the externalcurrent account from equation (1) then gives:CAB = (SpIp) + (SgIg) = SI (9)

Equation (9) shows that the private savings-investment balance plus the governmentfiscal balance equals the current account balance. It also implies that, if governmentsector dissaving is not offset by net saving of the private sector, the current accountwill be in deficit. More specifically, the equation shows that the budgetary position ofthe government (Sg-Ig) may be an important factor influencing the current accountbalance.

Government deficit is financed by borrowing from the domestic sector, borrowingfrom the external sector and borrowing from the central bank. All these factors haveinfluences on the domestic capital and financial markets and also on the balance of

payments.

Economists generally agree that a persistent fiscal deficit may ultimately spill overthe current account deficit in the balance of payments. On the converse, a sustainedcurrent account deficit may reflect persistent government spending in excess ofreceipts, and such excess spending may suggest that fiscal tightening is theappropriate policy action to tackle both fiscal and balance of payments problems.

We also know that

CAB = NKA+RT = SI (10)

Equation (10) shows that the current account balance is necessarily equal (with signreversed) to the net capital and financial account balance plus reserve assettransactions. This relationship shows that the net provision, as measured by thecurrent account balance, of resources to or from the rest of the world mustbydefinitionbe matched by a change in net claims on the rest of the world.It may be useful to rewrite CAB as:

SI = CAB = TB+SB+FIB+NCT = NKA+RT, where (11)

Where TB, SB, FIB and NCT stand for trade balance, services balance, factorincomes balance and transfer balance respectively.

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Box-1.1: Basic Structures of Macroeconomic Sub-models and Inter-linkages

8

REAL SECTORNational AccountsGDP at current market prices Private consumption General govt. consumption

General govt. investment Private investment Exports of goods and

Non-factor services Imports of goods and

Non-factor services

CENTRAL GOVERNMENTRevenues

Taxes and non-taxes

GrantsExpenditures Current CapitalOverall balanceFinancing

Domestic financing (net) Banking system Nonbanking sector External financing (net)

Balance of PaymentsCURRENT ACCOUNT Exports of goods andNon-factor services

Imports of goods andNon-factor services Factor services (net) Transfers (net)

Official Private

MONETARY SECTORMonetary Authorities Net Foreign AssetsNet domestic assets: Net credit to centralgovt. Credit to banks other items (net)

Reserve money

CAPITAL ACCOUNT

Direct investment

Medium/long-term

Capital (net)

Short-term capital (net)

Overall balanceChange in net foreign

assets

Deposit Money BanksBanks' reserves Net Foreign Assets

Net domestic assets: Net credit to central

govt. Credit to private sector other items (net)

Liabilities to monetaryauthorities

Private sector deposits

Prices, Inflation, Exchange Rate,

Deposit and Lending Rates


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1.3Inter-Sectoral Financial Transactions Matrix

In a paper entitled Balances, Imbalances and Fiscal Targets- a New Cambridge

Model Wynne Godley and Alex Izurietaof theCambridge Endowment for Research

in Finance (CERF), University of Cambridge used a simplified accounting matrix to

illustrate the interrelations of financial transactions among various sectors of the

economy. They argued that macroeconomic analysis would be easier if the main

income and expenditure flows comprising the GDP are arranged in a double entry

format as in Table-1.1. The two-way table indicates clearly the transactions among

any two economic agents viz. producers, consumers, government, and rest of the

world. The matrix shows how the gap between receipts and outlays of any sector

implies an equivalent rise or fall in its net acquisition of financial assets.

In this matrix the national income identity is shown, running vertically down in column

2, as the appropriation account of a postulated production sector. It says that gross

domestic product, Y, is equal to private expenditure, Cplus government expenditure,

G, plus exports,X, less imports, M. Every item in the GDPidentity has a counterpart

with the opposite sign in some other column. Taxes less transfers, T, are received or

paid by the government; net property income, taxes and transfers, TFand TP, are

paid by respectively the external and private sectors. The total in line 7 shows that

public borrowing, PSNB, equals the private net acquisition of financial assets, NAFA,(that is saving less investment, or net saving) minus the balance of payments

Govt.

Households/

Consumption

Consumption

ExpenditureIncome

Govt.

Expenditure

Financial

Market

Saving

Invest-

mentRest of

the

World

Imports

Exports

Figure-1.1Circular Flow of Income and Expenditure

Taxes

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surplus, BP or plus the deficit. Wynne Godley and Alex Izurieta illustrated the

analysis with the help of UK and USA data. One important conclusion of the analysis

is that the financial balances (relative to income flows) must stay within certain limits

for maintaining the sustainability of public debt over time. This in turn implies that

that a strict monitoring of these basic balances is essential for formulation of effectivemacro stabilization policies.

Table-1.1: A Simplified Macro Transactions Matrix

Income/

Expenditure

Production Government Rest of

the World

Total

1.Pvt. Exp -C +C 0

2.Gov. Exp +G -G 0

3.Exports +X -X 0

4.Imports -M +M 0

5.GDP +Y -Y 0

6. Taxes,

Fact. Pay.

-TP +T -TF 0

7.Financial

Balances

+NAFA

= Y-C-TP

0 +PSNB

=T-G

-BP

=M-X-TF

0

2. Data Base

The basic data for 160 variables for the years 1975-20095 are obtained from the

official sources. Basic data on national accounts are obtained from the Central

Bureau of Statistics (CBS), Nepal supplemented by the data from the Nepal Rastra

Bank. Data on government finance statistics are obtained from the Nepal Ministry of

Finance and the Nepal Rastra Bank. Data on monetary and financial statistics and

the balance of payments statistics are obtained from the Nepal Rastra Banksupplemented by the data from the International Monetary Fund. Data on sectoral

employment and investment are estimated by the national consultants Prof. Durga

Lal Shrestha and Prof. Vikas Raj Satyal by using standard interpolation and

extrapolation techniques on the basis of limited data obtained from official sources.

So the data base can be considered to be authentic and reliable.

5 The year 1975 refers to the fiscal year ending with mid-July 1975 i.e. the fiscal year from mid-July 1974 to

mid-July 1975. Similarly, the year 2009 refers to the fiscal year ending with mid-July 2009 i.e. the fiscal year

from mid-July 2008 to mid-July 2009.

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The basic concepts and sources of variables and necessary adjustments are

indicated in Annex-1. The identities and descriptions of all variables are given in the

Annex-2. It may please be noted that all variables in the real sector start with the

letter y, those in the BOP sub-sector start with word bop, those in the

government finance start with gf and those in the monetary sector start with m.The names of other variables are self explanatory. The actual data sets are

presented in Annex-3 and the standard descriptive statistics of the variables are

presented in Annex-4.

2.1 Data Base on National Accounts for 1975-2009

It presents time series data on sectoral GDP at both current and constant 2000-01

prices in national currency (millions of Nepalese rupee) for the years 1975-2009. It

has data on both broad sectors of the economy viz. agriculture, industry and

services, and sub-sectors within these broad sectors. No sub-sector is considered

for Agriculture due to lack of long term series, while industry is sub divided into four

sub-sectors viz. mining and quarrying, manufacturing, construction and public utilities

(comprising electricity, gas and water supply), and services sector is sub divided into

four subsectors viz. wholesale and retail trade and hotels and restaurants; transport,

storage and communications; finance and insurance and real estate; and social

services (comprising health, education, public administration, community, social and

personal services).

In terms of current market prices, sectoral data are available for nine sub-sectors as

mentioned above. However, real GDP (at constant 2000-01 prices) are available foronly three broad sectors viz. agriculture, industry and services. Therefore, four types

of GDP deflators have been derived for agricultural, industrial, service and overall

GDP by the formula: GDP Deflator= 100*GDP at current market price/ GDP at

constant market price.

2.2 Data on Balance of Payments for 1975-2009

Data on Balance of Payments for the years 1975-2009 are obtained from the Nepal

Rastra Bank, supplemented by the data from the IMF. Necessary adjustments have

been made to take care of changes in the basic concepts, definitions andclassifications over the years (please see Annex-1 for methodology for detailed

adjustments).

2.3 Data on Monetary Survey and Prices for 1975-2009

Data on Monetary Survey, Financial Statistics and CPI for the years 1975-2009 are

obtained from the Research Department of the Nepal Rastra Bank.

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2.4 Data on Government Fiscal Operations for 1975-2009

Basic data on government financial statistics for the years 1975-2009 are taken from

the Nepal Rastra Bank supplemented by the data from the Ministry of Finance.

Necessary adjustments have been made to take care of changes in the basic

concepts, definitions and classifications over the years.

3 Basic Presumptions for Projections

As discussed in the Inception Report on Modeling prepared in October 2009, despite

the global financial crisis and economic recession, the Nepalese economy performed

well in 2008 and achieved real growth of 5.3 percent aided by agricultural growth at

4.7 percent, industrial growth at 1.8 percent and services growth at 7 percent.

Inflation was contained at 7.7 percent and there was a surplus on the current

account of the balance of payments. The reasons for no significant adverse impact

of global financial crisis on the Nepalese economy include the following:

(a) Nepals financial sector has limited external liabilities and assets.(b) Although its external current account has close links with the rest of the world,

the major link is with India which maintained relatively high growth ratesduring the crisis period.

(c) Nepal and India also maintained normal trade links during the crisis period.

Although the global slowdown did not have much adverse impact on the Nepalsfinancial sector, the real sector growth in 2008/09 was affected adversely by thedomestic factors such as unfavorable monsoon, power shortage and labor unrest. As

per the estimate of the Central Bureau of Statistics (CBS) the real GDP growth rateat basic prices decelerated from 5.3% in FY2008 to 3.8% in FY2009. The slowdownis broad-based, encompassing agriculture, industry and services. The agriculturesector grew by only 2.2% in FY2009, down from 4.7% in FY2008; industrial sector by1.8% almost the same as 1.9% in FY2008; and service sector by 5.8%, significantlydown from 7.0% in FY2008.

There was significant acceleration of the consumer price inflation from 7.7% in 2008to 13.2% in 2009 contributed by food inflation of 14.8 percent and non-food inflationof 9.8 percent. However, the fiscal situation and the balance of payments havesurplus on current accounts and macroeconomic fundamentals are sound. Both thegovernment and the monetary authority deserve to be complemented for maintainingeconomic stability in a difficult socio-economic-political context.

There are now indications that these adverse factors also continued in 2010. As per

the estimates made by the ADB Nepal Resident Office, real GDP growth rate is

expected to remain subpar at approximately 3.5% in FY2010 (16 July 2009 15

July 2010). Services are expected to expand robustly, but may not sufficiently offset

the weak performance of industry and agriculture. Industrial growth may remain

subdued due to continued political disturbances, frequent power cuts, and fuel

shortages. Assuming normal weather conditions during the winter, agriculture outputgrowth is also expected to decelerate from last year in view of significantly reduced

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summer crop output due to poor weather. There is liquidity crunch in the banking

sector caused by a number of factors such as decelerating remittances, heavy

imports and excessive lending by commercial banks. Fuelled by high import prices

and high CPI inflation in India, domestic inflation is also running at high levels, and

challenging macroeconomic stability during 2010. Consequently, there was upwardtrend of deposit and lending rates in contrast to the declining trend in the recent past.

Despite the political turmoil and the labor unrest, both the government and the Nepal

Rastra Bank deserve complements for adopting sound macroeconomic and prudent

monetary policies to tackle the adverse impact of the global financial crisis and

economic recession and spiraling inflation in Nepal.

In the Budget estimate for 2010, the Ministry of Finance projected a rise of the

expenditure outlay by 37% and revenue by 37% over FY2009. Although the targets

were ambitious, the mid-term review of the budget carried out by the Ministry of

Finance in February 2010 suggested that both expenditure and revenue targets arelikely to be met.

Our forecasts of key macro-economic variables for the years 2011-2015 take into

account the developments in the current year 2010. We further assume that the

transitional problems of strikes and labor unrest will be solved by the next year. In

addition, our base line projections are based on the following presumptions:

(a) Government will continue with the prudent macroeconomic management.(b) There will be no weather socks;(c) Ongoing political problem will soon be over and there will be politicalstability to create enabling environment for private investment including foreigninvestment;(d) There will be no other internal and external shocks, except volatile oilprices.(e) Tourists arrivals and travel incomes would return to normal levels;(f) Remittances would continue to be buoyant;(g) Donors support to Nepal would continue at normal levels;(h) There will be no change in the real exchange rate of the Nepalese rupeeduring the projection period.

4. Test and Calibration Techniques

4.1 Standard descriptive statistics

First of all, we examined the standard descriptive statistics for all variables (seeAnnex-3). These include Mean, Median, Maximum, Minimum, Standard deviation,degree of Skewness, degree of Kurtosis, and Jarque-Bera statistics for test ofnormality. An analysis of these statistics lead to the conclusion that most of thevariables are positively skewed (i.e. Mean>Median) and leptokurtic (degree ofKurtosis>1) implying more values around the mean than in the tails, and deviatefrom normal distribution. 85 out of 161 variables showed some problems in Normality

test.

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4.2 Test of Trend Stationarity

The classical Least Squares (CLS) is based on the assumption that bothendogenous and exogenous variables are stationary. Hence, tests are needed toverify if the variables are stationary or not. If some variables are not stationary, they

are needed to make stationary by various techniques such as using autoregressive(AR) and/or moving average (MA) components.

For stationarity test we have applied the KPSS method which is the direct method forthe test of trend stationarity, and observed that most of the variables are trendstationary at the level. However, we have not examined other tests on the existenceof unit root or stationarity such as the ADF test, PP test etc. One of the most popularunit root tests is the Dickey Fuller (DF) or its slight modification the AugmentedDickey Fuller (ADF) test. But, econometric literature has indicated many limitations ofthis test- particularly its lack of power to test random walk. Econometricians alsoindicate the following limitations of ADF test:

a) DF test is a parametric test that uses Tau -statistics as its underlyingdistribution. However, Tau cannot be approximated to t-distribution in manydifferent situations.

b) Similarly ADF test uses restricted F-statistics as its underlying distributionwhich again cannot be approximated to F distribution in many differentsituations. In such situations the critical value cannot be used for comparison.

c) DF test assumes that ut are uncorrelated. So the test cannot give meaningfulresults if we have autocorrelation in ut.

d) Diebold and Rudebusch (1991) show that the DF/ADF unit root tests have lowpower against fractionally-integrated processes.

e) De Jong et al. (1992) argue that the Dickey-Fuller unit root test has lowpower against trend stationary processes.

f) Perron (1989) argue that if the time-series contains a structural break, thenthe DF unit root tests will lead to the acceptance of the null of a unit root,when in fact the series is stationary.

Consequently, many economists have argued against using the DF/ADF unit roottests and proposed other powerful tests, such as the direct tests that can be usedto test the null hypothesis of stationarity against the alternative of non-stationarity. In this respect, the most popular one is the KPSS test developed by

Kwiatkowski, Phillips, Schmidt, and Shin (1992)6

. It is also most meaningful whenone fits multiple regression equations, not merely an autoregressive model.

KPSS test is based on the null hypothesis that the time series being tested is

stationary around a deterministic trend. All the tests of KPSS performed by us were

at the leveled form and with trend and intercept. Our tests indicate that all series are

stationary at the level at the level within 10% level of significance, except four

variables which are stationary at first difference.

6

Kwiatkowski, D. et al. (1992), Testing the Null Hypothesis of Stationarity against the Alternativeof a Unit Root, Journal of Economics, 54, 159-178.

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4.3 Choice of Variables and Direction of Causality

The dependent and independent variables were chosen by standard economic

principles as per macroeconomic theories. It is not possible to discuss all thesetheories, as the economic literature is vast. Basically, the model is based on the

circular flows of income and expenditure in the economy (as depicted in Figure-1.1

above), which have feedbacks on each other. Choice of dependent and

independent variables was facilitated by the Granger causality test7. But, it should

be kept in mind that the Granger causality is a statistical causality indicating

precedence and information content but does not by itself indicate causality in the

more common use of the term8.

4.4 Pearson Correlation Matrix

Prearson correlation matrix9 for a group of independent variables was also estimated

to examine whether there exists problems of multicollinearity among the variables. In

general, if the multiple correlation coefficient (adjusted for the degrees of freedom)

happens to be lower than the partial correlation coefficient between two variables,

then there exists the problem of multicollinearity among these variables, and the

associated regression coefficients will be either overestimated or underestimated.

4.5 Selection of Statistical Method for Estimation

We decided to use the ordinary least squares (OLS) or ARIMA models for estimationand to test whether the standard assumptions of OLS are satisfied. It may bementioned here that the use of Ordinary Least Squares (OLS) is in fact supported bylarge scale macroeconomic modeling10. In most situations, given constraints onresources in terms of money, time, technical manpower and the available hardwareand software, a modeler has to make a compromise between what is technically

7Null hypothesis for the Granger causality test is X does not Granger cause Y. If the calculated F is

small and consequently the associated probability is larger than 0.05, then at 5% level we accept the

null hypothesis that X does not Granger cause Y. Conversely, if probability is smaller than 0.05 we

conclude that the null hypothesis is rejected indicating a significant direction of causation from X to Y.

If both X Granger causes Y and Y Granger causes X, we need to adopt the simultaneous system

of relations.

8 Granger causality analysis is a method for investigating whether one time series can correctly

forecast another (Granger, 1969). This method is based on multiple regression analysis. The Granger

approach to the question of whether x causes y is to see how much of the current can be explained by

past values of y and then to see whether adding (d) 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 xs are statistically significant. The statement x Granger causes ydoes not

imply that y is the effect or the result of x. Granger causality measures precedence and information

content but does not by itself indicate causality in the more common use of the term.

9

The most widely-used type of correlation coefficient is Pearson r (Pearson, 1896), also called linear orproduct-momentcorrelation. The coefficient measures the degree of relation among two variables only when the

underlying relation is linear.

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desirable and what is operationally feasible and widely usable for sustainability.Although minimum statistical and professional standards need to be satisfied forpeer review, such standards need to be practicable, simple and widely understood.

Given the above mentioned criteria, modelers often prefer OLS for large scale

macro-econometric modeling. OLS is user friendly and easily understood by bothtechnical and non-technical policy makers. OLS is also best suitable to capture inter-relations among various variables and for projections and simulations.

However, OLS should not be used when the basic conditions are not satisfied. Forall our equations, the basic statistical tests are fully satisfied for OLS.

4.5 Use of Auto Regressive Models

First of all, correlograms table was examined for a series. If there was

autocorrelation for a variable, the following criterion was adopted to choose the

correct form of ARIMA.

Identification of ARIMA Model by examining Serial Auto Correlation Factor

(SACF) and Partial Autocorrelation Factors (PACF)

Process SACF PACF

AR(p) Geometrically declining Curtailed after lag p

MA(q) Curtailed after lag q Geometrically declining

ARMA(p,q) Declining after lag q Declining after lag pSource: Madsen, Henning (1992) Forecasting Econometric Time Series- An Introduction.

It was observed that for most of the variables, SACF was geometrically declining and

the PACF was curtailed after lag 1. So for most of the exogenous variables, AR(1)

was taken as the suitable model. For a few variables, AR(1) and MA(1) model was

accepted for calibration.

10 (a) A Quarterly Macroeconometric Model of the Turkish Economy, by Cem Aysoy and Ahmet N.Kpc, Research Department, Central Bank of the Republic of Turkey, 2005.(b) Shocks, EconomicGrowth and the Indian Economy, BB Bhattacharya and Sabyasachi Kar, Reserve Bank of India, 2004.(c) Macroeconomic effects of public investment in infrastructure in India, K. N. Murty and A. Soumya,RBI Indira Gandhi Institute on Development Research, 2006.

However, a number of countries like the Thailand Monetary Committee, the Philippines and Slovaniahave conducted usual tests (such as Unit Root and Cointegration) before using the OLS Method.

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4.6 Statistical Tests for the Fitted Equations

The best fitted relations are decided on the basis of following criteria:

(a) Highest value of R-Bar-square

(b) Significant t-values for the regression coefficients(c) F-statistic: tests the hypothesis that all the slope coefficients (excluding theconstant) in a regression are equal to zero.

(d) Prob (F-statistic)

(e) Mean Absolute Percentage Error and Theil Inequality Coefficients-These two statistics are scale invariant, and smaller the smaller the error, thebetter the forecasting ability of that model according to the criterion. The Theilinequality coefficient always lies between zero and one, where zero indicatesa perfect fit.

(f) Bias Proportion, variance proportion and covariance proportion- thesethree proportions add up to unity. The bias proportion tells us how far themean of the forecast is different from the mean of the actual series. Thevariance proportion tells us how far the variation of the forecast is differentfrom the variation of the actual series. The covariance proportion measuresthe remaining unsystematic forecasting errors. For good forecast, the biasand variance proportions should be small so that most of the bias should beconcentrated on the covariance proportions.

(g) Test of autocorrelation- In general, Durbin Watson Statistic is used to testautocorrelation in the residuals. Ideally, DW should be around 2. However, if

the regression model contains AR(p) as an independent variable, then DWstatistic does not provide the real test. Then serial correlation may beexamined by: View Residual Test Correlogram-Q-statistics. TheEViews will show the autocorrelation function (AC), the partial autocorrelationfunction (PAC), the Box-Ljung Q statistic (Q-stat) and its p-value. If the p-values are small for all lags, there is no serial correlation.

(h) If the multiple R-square exceeds the Durbin Watson Statistic, then there is theproblem of non-stationarity of the variables. So such equations have beenaccepted for projections.

(i) We also examined the actual values of the dependent variable, the fitted

values, and the residuals by: View

Actual,Fitted,Residual

Actual,Fitted,Residual Table

4.7 Fitted Equations and Tables on Eviews7

All the fitted equations and tables have been listed in the Eviews7 Workfile. Initially,

alternative equations were tried with many variables. But, non-significant variables

have been dropped, and only significant equations have been kept in the file.

However, in some cases there are alternative significant equations. Ultimately, we

have selected one among these equations on the basis of economic and statistical

criteria and some value judgements for ultimate projection and simulations.

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5. Computer Algorithms

Test, calibrations, simulations and projections have been done with the help of

EViews7 software. Excel data files have been converted to Eviews7 workfile.

Variables, identities and equations have been coded and programmed on EViews7.

All necessary inputs on EViews have been prepared to prepared the required Visualdisplay of the model. However, the work on preparing the Visual Display is

incomplete due to non-procurement of the necessary vendor for preparation of the

visual display software. However, the test, calibration, simulation and projections of

the model were completed only on the 29th May 2010. Engagement of vendor before

completing the modeling calibration would have been very expensive. Therefore,

non-engagement of the vendor is justied on economic considerations.

So, in a way, there was time constraint to complete the whole work. Only seven

weeks were available to gather and validate data, specify, test, calibrate the

equations and make simulations for alternative policy options. Seven weeks are not

sufficient to build and calibrate such a large sized macro economic model.

The remaining works on visual display may either be completed in the intermittent

period with the help of the local IT Specialist or be completed in the third phase

which will be mainly utililized for the development, test, calibration, projections and

simulations of a Dynamic Stochastic General Equilibrium Model (DSGEM).

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Statistical Annex Tables

Annex-1: Basic Concepts and Sources of Data andAdjustments

Annex-2: Description of Variables and Identities

Annex-3: Data Base for 1975-2009

Annex-4: Standard Descriptive Statistics

Annex-5: A short description of the statistical tests used for

modeling

Annex-6: Summary Results for the KPS Unit Root Tests for

Trend Stationarity of the Variables

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grouped as social sectors comprising health, education, public administration,personal, religious, community and other social services.

Similarly, GDP by expenditure category has been restructured since the date.Consumption expenditure has been classified into government consumption, private

consumption and non-profit institutions. Government consumption expenditure isfurther sub-classified into collective and individual consumptions and the privateconsumption is sub-classified into food, non-food and services.

Government Finance Statistics

The structure of the government finance has also been changed since the fiscal year2001/02. The government expenditure has being classified into recurrent, capitaland principle payment instead of conventional classification into regular anddevelopment expenditure. The regular and development expenditures before thefiscal year 2001/02 have been transformed into recurrent and capital expenditures

and principal payment with required adjustments.

Balance of Payments (BOP) Statistics

The balance of payment data have also been changed since the fiscal year1999/2000. Both export and import of goods are classified into oil and non-oilcomponents. (Factor) Income has been separated from the (non-factor) servicetrade. The financial account has been treated as a separate sub heading and haspresented in detailed structures from the capital account (capital transfers).

Adjustments

Thus, the structures of national account statistics, government finance and balanceof statistics have been improved since the end of twentieth century and starting ofthe twenty first century. These improvements and changes in concepts andclassifications have been considered and have necessitated appropriate adjustmentsin the time series data for making them comparable and consistent over time.Besides, some national account statistics, before and after the fiscal year 1984/85,are found to be a little bit inconsistent due to the methodological changes in thenational accounting system adopted by the CBS. However, the time series data from1974/75 have been used since the applications of EViews7 software require at least

30 years data for better statistical estimation and analysis of underlying trends..Sectoral Gross Fixed Capital Formation

The CBS publishes only the government and private gross fixed capital formation.Sectoral gross fixed capital formation is not published. Sectoral gross fixed capitalformations for period of 1974/75 to 2008/09 have been estimated based on itsannual control totals published by the CBS.

Population

Population is calculated using figures from the decennial census of CBS. For theintermediate years interpolation method with compound growth rate has been used...

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For population after 2001, medium variant population growth is used as projected inthe CBS website.

Agricultural Crop Area and Inputs

Data relating to the agriculture sector namely, total cropped area (000 hectares),extension of additional Irrigated area ('000 hectares), seed(MT) and chemicalFertilizer (MT) have been collected from the different series of Economic Surveypublished by the Ministry of Finance. Similarly, the rainfall data has been collectedfrom the different series of Statistical Year Books published by the CBS. The WPI ofthe world has been collected from the IMF year book from IMF website. Similarly,Indian CPI and WPI have been taken from the website of the Reserve Bank of India.

Sectoral Employment

Data relating to sector employment are not available in the country. Population

censuses give only the economically active population, which are not actuallyemployment figures. However, the problem of data lacking has made us bound totake economically active population as the proxy of the employment. Thats why;sectoral employment has been estimated based on the sectoral growth rate betweenthe two Nepal Labor Force Surveys of 1998/99 and 2008 for the period of 1991/92 to2008/09 and for prior 1991/92 sectoral employment has been projected backwardbased on the inter-population census growth rate of economically active populationand the sectoral employments in 1991/92 estimated based on the growth rates ofrespective sectors using these two labor force surveys assuming that the intercensus growth rate of economically active population were the inter census growthrate of employment of the respective sectors. It is necessary because there is noother option. Otherwise, methods of interpolation and extrapolation based on twosurveys will give us only linear growth rate of the employment of respective sectors,which may not be realistic. .

Bibliography

1. CBS, 1984. Statistical Pocket Book Nepal 1984. His Majesty Government,National Planning Commission Secretariat, CBs, Kathmandu, Nepal.

2. CBS, 1987. Statistical Year Book of Nepal 1987. His Majesty Government,National Planning Commission Secretariat, CBs, Kathmandu, Nepal.

3. CBS, 1994. Statistical Pocket Book Nepal 1994. His Majesty Government,National Planning Commission Secretariat, CBS, Kathmandu, Nepal.4. CBS, 1998. Statistical Pocket Book Nepal 1998. His Majesty Government,

National Planning Commission Secretariat, CBS, Kathmandu, Nepal.5. CBS, 1999. Report on the Nepal Labor Force Survey 1998/99. His Majesty

Government, NPC Secretariat, CBS, Kathmandu, Nepal.6. CBS, 1999. Report on the Nepal Labor Force Survey 1998/99. His Majesty

Government, NPC Secretariat, CBS, Kathmandu, Nepal.7. CBS, 2008. Report on the Nepal Labor Force Survey 2008. His Majesty

Government, NPC Secretariat, CBS, Kathmandu, Nepal.8. CBS, 2005. Statistical Year Book of Nepal 2005. His Majesty Government,

NPC Secretariat, CBS, Kathmandu, Nepal.

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9. MOF, 1999. Economic Survey: Fiscal Year 1989/99. His MajestysGovernment, Ministry of Finance, Kathmandu, Nepal.

10.MOF, 2000. Economic Survey: Fiscal Year 1999/00. His MajestysGovernment, Ministry of Finance, Kathmandu, Nepal.

11.MOF, 2002. Economic Survey: Fiscal Year 2001/02. His Majestys

Government, Ministry of Finance, Kathmandu, Nepal.12.MOF, 2004. Economic Survey: Fiscal Year 2003/04. His Majestys

Government, Ministry of Finance, Kathmandu, Nepal.13.MOF, 2005. Economic Survey: Fiscal Year 2004/05. His Majestys

Government, Ministry of Finance, Kathmandu, Nepal.14.MOF, 2006. Economic Survey: Fiscal Year 2005/06. Government of Nepal,

Ministry of Finance. Kathmandu, Nepal.15.MOF, 2007. Economic Survey: Fiscal Year 2006/07. Government of Nepal,

Ministry of Finance. Kathmandu, Nepal.16.MOF, 2008. Economic Survey: Fiscal Year 2007/08. Government of Nepal,

Ministry of Finance. Kathmandu, Nepal.

17.MOF, 2009. Economic Survey: Fiscal Year 2008/09. Government of Nepal,Ministry of Finance. Kathmandu, Nepal.

18.NPC, 2002. Tenth Plan (2002-2007). His Majestys Government of Nepal,National Planning Commission. Kathmandu, Nepal.

19.NPC, 2007. Three Yearly Interim Plan (2007/08-2009/10). Government ofNepal, National Planning Commission. Kathmandu, Nepal.

20.NRB, 2001. Quarterly Economic Bulletin. Mid-July, 2001. Volume XXXV.Number 3&4. Nepal Rastra Bank. Kathmandu, Nepal.

21.NRB, 2009. Current Macroeconomic Situation (Based on the First Fivemonths Data of 2009/10). Research Department, Nepal Rastra Bank.Kathmandu, Nepal.

22.NRB, 2009. Quarterly Economic Bulletin Mid-January 2009-Mid-July 2009.Volume 44. Number 3&4. Nepal Rastra Bank. Kathmandu, Nepal.

23.NRB, 2009. Quarterly Economic Bulletin volume 43. Number 1&2. NepalRastra Bank. Kathmandu, Nepal.

24.NRB, 2010. Monetary Policy of Fiscal Year 2009/10: Mid Term Review. NepalRastra Bank. Kathmandu, Nepal.(In Nepali)

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A nnex-2

Variables used for the Upgraded Nepal Macroeconomic Model

Serial

No. Variable

Description Unit Type

1 Time 1 to 35 with Year 1974-75=1 Exogenous

2 Population Population Million Endogenous

Real sector and National AccountsBlock

3ycst

Overall GDP at constant 2000-01 basicprice =ycstagr+ycstind+ycstser-ycstfism

Million Rs Identity

4ycstagr

Agriculture GDP at constant 2000-01factor cost

Million Rs Endogenous

5ycstind

Industry GDP at constant 2000-01 factorcost


6ycstser

Services GDP at constant 2000-01 factorcost


7ycstfism

Financial. intermediary Services GDP at2000-01 price


8ycstnonagr

Non-agriculture GDP at 2000-01 basicprice=ycst-ycstagr

Million Rs Identity

9ycmp

Overall GDP at current marketprices=yc+yinv+yxgs-ymgs

Million Rs Identity

10Ygni

Gross national income= ycmp+bopincome

Million Rs Identity

11Ygndi

Gross national disposable income= ygni + boptransfer

Million Rs Identity

12yc

Consumption at current market prices= ypc + ygc

Million Rs Identity

13 ypc Private consumption at current marketprices Million Rs Endogenous

14ygc

Government consumption at currentmarket prices


15yinv

Gross domestic investment =ygfcf+ystock Million Rs Identity

16ygfcf

Gross fixed capital formation=ygfcfpub+ygfcfpvt

Million Rs Identity

17ygfcfpub

Public Gross fixed capital formation Million Rs Endogenous

18ygfcfpvt

Private Gross fixed capital formation Million Rs Endogenous

19ystock

Change in stocks and inventories Million Rs Endogenous

20yxgs

Exports of goods and services=bopexp+bopsercr

Million Rs Identity

21ymgs

Imports of goods and services=bopimp+bopserdb

Million Rs Identity

22yfc

Overall GDP at current price=gfcagr+gfcind+gfcser-fism

Million Rs Identity

23yfcnonagr

Non-agriculture GDP at current factor cost=yfc-yfcagr

Million Rs Identity

24yfcagr

Agriculture GDP at current factor cost Million Rs Endogenous

25yfcind

Industry GDP at current factor cost=yfcmin+yfcmanf+yfcutility+yfcconst

Million Rs Identity

26 yfcmin Mining and Quarrying GDP at currentfactor cost Million Rs Endogenous

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27yfcmanf

Manufacturing GDP at current factor cost Million Rs Endogenous

28yfacutility

Utility (electricity, gas, water) GDP atcurrent factor cost


29yfcconst

Construction GDP at current factor cost Million Rs Endogenous

30 yfcser Services GDP at current factor cost=yfctrade+yfctrans+yfcfin+yfcsocial Million Rs Identity

31yfctrade

Retail and wholesale trade GDP atcurrent factor cost


32yfctrans

Transport and communications GDP atcurrent factor cost


33yfcfin

Financial services and real estate GDP atcurrent factor cost


34yfcsocial

Social, community & pub. admn GDP atcurrent factor cost


35yfcfisim

Fin. Intermediary services GDP at currentprice


36

yfcindtax

Indirect taxes less subsidies Million Rs Endogenous

37ymp

GDP at current market prices= yfc + yfcindtax

Million Rs Identity

38ympnonagr

Non-agriculture GDP at current marketprices = ymp-yfcagr

Million Rs Identity

39inv

Overall investment = gfcf=invagr+invind+invservice

Million Rs Identity

40invagr

Agriculture investment Million Rs Endogenous

41invind

Industry investment= invmin+invmanf+invutility+invconst

Million Rs Identity

42invmin

Mining and quarrying investment Million Rs Endogenous

43invmanf

Manufacturing investment Million Rs Endogenous

44invutiliy

Public utility investment Million Rs Endogenous

45invconst

Construction investment Million Rs Endogenous

46invser

Services investment= invtrade+invtrans+invfin+invsocial

Million Rs Identity

47invtrade

Wholesale and retail trade investment Million Rs Endogenous

48invtrans

Transport and communicationsinvestment


49

invfin

Financial sector investment Million Rs Endogenous

50invsocial

Social and Other services investment Million Rs Endogenous

51

agcularea

Agricultural cultivated area Exogenous

52agcroparea

Agricultural cropped area Thousandhectares

Exogenous

53agirrarea

Agriculture irrigated area Hectares Exogenous

54agSeed

Agriculture seeds Metric tons Exogenous

55

agFert

Agriculture fertilizers Metric tons Exogenous

56emp

Overall employment= empagr+empind+empservice

1000 persons Identity

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57 Empagr Agriculture employment 1000 persons Endogenous

58Empind

Industry employment=empmin+empmanf+emputility+empconst


59Empmin

Mining and quarrying employment 1000 persons Endogenous

60

Empmanf

Manufacturing employment 1000 persons Endogenous

61Emputility

Public utility employment 1000 persons Endogenous

62EmpConst

Construction employment 1000 persons Endogenous

63empser

Services employment=emptrade+emptrans+empfin+empsocial


64Emptrade

Wholesale and retail trade employment 1000 persons Endogenous

65Emptrans

Transport and communicationsemployment

1000 persons Endogenous

66Empfin

Financial sector employment 1000 persons Endogenous

67

Empsocial

Social and Other services employment 1000 persons Endogenous

Government Finance Block

68GFEXP

Government expenditure= gfcrexp+gfcapexp+gfamort

Million Rs Identity

69gfcrexp

Government current expenditure Million Rs Exogenous

70gfcapexp

Government capital expenditure Million Rs Endogenous

71gfamort

Amortization = repayment of principal Million Rs Endogenous

72gfreceipt

Government receipts including grants= gfrev+gfgrants

Million Rs Identity

73

gfrev

Govt revenue = gftax + gfnontax Million Rs Identity

74gftax

Govt tax revenue = gfdirtax + gfindtax Million Rs Dentity

75gfnontax

Govt non-tax revenue Million Rs Endogenous

76gfgrants

Govt grants Million Rs Exogenous

77gfbalance

Govt budget balance= gfreceipt-gfexp

Million Rs Identity

78gffloan

Govt foreign loan Million Rs Exogenous

79gfdloan

Govt domestic loan Million Rs Endogenous

80 gfcashbal Govt cash balance= gfbalance- gffloan-gfdloan Million Rs Identity

81gfindtax

Govt indirect tax=gfcustoms+gfgoodstax

Million Rs Identity

82gfcustom

Customs revenue =gfImpduty +gfexpduty+gfexrefund+ gfothers

Million Rs Identity

83gfimpduty

Import duty Million Rs Endogenous

84gfexpduty

Export duty Million Rs Endogenous

85gfexrefund

Excise refund from India Million Rs Endogenous

86 gfcusother

s

Other customs duty Million Rs Endogenous

87gfgoodstax

Goods tax = gfexcise+gfvat Million Rs Identity

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88gfexcise

Excise duty Million Rs Endogenous

89gfvat

Value added tax Million Rs Endogenous

90gfgothers

Others services tax Million Rs Endogenous

91 gfgtotal Total goods and services tax=gfgoodstax+gfgothers Million Rs Identity

92gfdirtax

Direct tax=gflandstax+gftaxprinc

Million Rs Identity

93gflandtax

Lands tax=gfLandRev+gfHousetax

Million Rs Identity

94gflandrev

Land revenue Million Rs Endogenous

95gfhousetax

Property tax Million Rs Endogenous

96gftaxprinc

Tax on property profits and income Million Rs Endogenous

Balance of Payments Block

97 bopexp Goods exports Million Rs Endogenous

98bopimp

Goods imports Million Rs Endogenous

99bopgb

Goods balance=bopexp-bopimp Million Rs Identity

100bopservice

Net service Million Rs Identity

101bopsercr

Services credit Million Rs Endogenous

102bopserdb

Services debit Million Rs Endogenous

103bopincome

Net income from abroad Million Rs Identity

104bopincr

Income credits Million Rs Endogenous

105bopindb

Income debits Million Rs Endogenous

106boptransfer

Transfer net from abroad=boptrcr-boptrdb

Million Rs Identity

107boptrcr

Transfer credit=bopRemit+bopgrants+boptrothers

Million Rs Identity

108bopRemit

Remittances Million Rs Endogenous

109bopgrants

Grants Million Rs Endogenous

110 boptrOther

s

Other transfer Million Rs Endogenous

111boptrdr

Transfer debit Million Rs Endogenous

112bopcab

Current account balance =bopgb+bopservice+bopincome+boptransfer

Million Rs Identity

113bopcapbal

Capital and financial balance=bopgloan+fdi+bopfinother

Million Rs Identity

114bopgloan

Govt loan =bopfloan-bopamort Million Rs Identity

115bopfloan

Froreign loan Million Rs Endogenous

116bopamort

Amortization Million Rs Endogenous

117 bopfdi Foreign direct investment Million Rs Endogenous

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118bopfinother

Other financial flows Million Rs Endogenous

119bopbal

Overall BOP balance =bopcab+bopcapbal

Million Rs Identity

120bopresch

Reserve change = - bopbal Million Rs Identity

121 exrate$ Exchange rate= Nepalese rupees per US dollar Million Rs Endogenous

Money and Financial Sector Block Million Rs

122Mnfa

Net foreign assets Million Rs Endogenous

123Mnda

Net domestic assets = mcredit+mothers Million Rs Identity

124MCredit

Total credits= mcrgov+mcrpub+mcrpvt Million Rs Identity

125Mcegov

Credits to govt Million Rs Endogenous

126Mcrpub

Credits to the public enterprises Million Rs Endogenous

127MCRPVT

Credits to the private sector Million Rs Endogenous

128Mothers

Other items Million Rs Endogenous

129M2D

Broad money demand= mnfa+mnda

Million Rs Identity

130M2S

Broad money supply= m1+mfd Million Rs Identity

131M1

Quasi money supply =Mcurrency+mdd Million Rs Identity

132Mcurrency

Currency in circulation with the public Million Rs Endogenous

133

MDD

Demand deposits Million Rs Endogenous

134MFD

Fixed deposits Million Rs Endogenous

Prices, Interest Rates and ExchangeRate Block

Million Rs

135dfyfc

Deflator for overall GDP at basic price =100*yfc/ycst

Identity

136dfyagr

Deflator for agriculture GDP at basic price= 100*yfcagr/ycstagr

Identity

137dfyind

Deflator for industry GDP at basic price =100*yfcind/ycstind

Identity

138dyser

Deflator for services GDP at basic price =100*yfcser/ycstser

Identity

139drsav

Savings deposit rate Percentage Exogenous

140dr1yr

Deposit rate for 1 year fixed deposit Percentage Exogenous

141dr2yr

Deposit rate for 2 year fixed deposit Percentage Exogenous

142bankrate

Bank rate Percentage Exogenous

143lrind

Lending rate for industry Percentage Exogenous

144lragr

Lending rate for agriculture Percentage Exogenous

145

lrexp

Lending rate for exports Percentage Exogenous

146lrcom

Lending rate for commerce Percentage Exogenous

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147lravg

Average lending rate=(lragr+lrind+lrexp+lrcom)/4

Percentage Exogenous

148 cpi Consumer price index Base 1995=1 Endogenous

149

cpifood

CPI for food Base 1995=1 Endogenous

150 cpinonfood CPI for non-food Base 1995=1 Endogenous

151InfCPI

CPI inflation = rate of change of CPI=100*(cpi/cpi(-1)-1)

Percentage Derived

152InfFood

CPI Food inflation=100*(cpifood/cpifood(-1)-1)

Percentage Derived

153infNonFood

CPI NonFood inflation=100*(cpinonfood/cpinonfood(-1)-1)

Percentage Derived

154infdfyfc

Inflation in terms of GDP deflator=100*(dfyfc/dfyfc(-1)-1)

Percentage Derived

155infdfyagr

Inflation in terms of deflator for agr=100*(dfyagr/dfyagr(-1)-1)

Percentage Derived

infdfyindInflation in terms of deflator for ind=100*(dfyind/dfyind(-1)-1)

Percentage Derived

155 infdfyser Inflation in terms of deflator for ser=100*(dfyser/dfyser(-1)-1) Percentage Derived

157OilPrice

Crude oil price US$ per barrel Exogenous

158wpiWorld

World WPI Base2000=100

Exogenous

159cpiIndian

Indian CPI Base2001=100

Exogenous

160wpiInd

WPI in industrialized countries Base2000=100

Exogenous

161Infoil

Rate of change of oil price=100*(oilprice/oilprice(-1)-1)

Percentage Derived

162

Infworld

Inflation in terms of world WPI

=100*(wpiWorld/wpiWorld(-1)-1)

Percentage Derived

163Infindian

Inflation in terms of Indian CPI=100*(cpiIndian/cpiindian9-1)-1)

Percentage Derived

164infwpiind

Inflation in terms of industrial countriesWPI =100*(wpiInd/wpiInd(-1)-1)

Percentage Derived

165Ygni$

Ygni in terms of US$ Million $ Derived

166ypcnr

Percapita income in terms of NepaleseRupees

Rs Derived

167Ypc$

Percapita income in terms of US$ US$ Derived

Annex-3:

Data Base for 1975-2009

Year Population

ycst ycstagr ycstind ycstser ycstfism Gni

1,975 12 139439 78627 12012 41314 7,485 16,5711,976 13 143101 79069 12556 45642 5,833 17,4581,977 13 142628 75843 14059 49660 3,067 17,3541,978 13 146115 75843 15681 52373 2,218 19,8191,979 14 149453 78150 15340 52903 3,060 22,3151,980 14 147473 74427 15488 57373 186 23,5011,981 15 161439 82140 17202 60848 1,249 27,4281,982 15 169386 85884 18981 62734 1,786 31,150

1,983 15 170244 84944 21148 62695 1,457 33,7611,984 16 185594 93045 22946 67591 2,012 39,339

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Year gndy ycmp yc ypc Ygc yinv ygfcf

1,975 17,095 16,571 14,909 13,652 1,257 2,402 2,223

1,976 18,047 17,394 15,354 14,060 1,294 2,632 2,443

1,977 17,972 17,280 14,948 13,688 1,260 2,769 2,580

1,978 20,407 19,732 17,192 15,721 1,471 3,507 3,295

1,979 23,184 22,215 19,630 17,741 1,889 3,514 3,263

1,980 24,689 23,351 20,760 19,195 1,565 4,270 3,681

1,981 28,845 27,307 24,333 22,411 1,922 4,808 4,299

1,982 32,781 30,988 27,910 25,272 2,638 5,314 5,465

1,983 35,652 33,761 30,874 27,458 3,416 6,628 6,576

1,984 41,412 39,290 35,504 31,860 3,644 7,251 6,907

1,985 50,157 46,587 40,348 35,977 4,371 10,184 9,386

1,986 57,975 55,734 49,847 44,782 5,065 10,599 9,431

1,987 66,536 63,864 56,543 50,746 5,797 12,898 11,825

1,988 79,650 76,906 69,302 62,407 6,895 15,237 13,414

1,989 92,200 89,270 79,120 70,173 8,947 19,415 16,3921,990 106,714 103,416 95,273 86,314 8,959 19,076 17,002

1,991 124,429 120,370 108,856 97,771 11,085 25,073 22,780

1,992 154,374 149,487 133,280 121,372 11,908 31,619 29,2761,993 178,784 171,474 148,302 133,402 14,900 39,653 37,278

1,994 206,670 199,272 170,052 154,065 15,987 44,644 42,032

1,995 230,156 219,175 186,710 166,443 20,267 55,231 48,370

1,996 260,962 248,913 214,487 191,469 23,018 68,017 56,081

1,997 295,926 280,513 241,351 216,364 24,987 71,084 60,794

1,998 318,947 300,845 259,407 231,392 28,015 74,728 65,375

1,999 365,770 342,036 295,473 264,944 30,529 70,061 65,269

2,000 436,903 379,488 321,911 287,947 33,964 92,272 73,324

2,001 508,814 441,518 390,018 354,233 35,785 98,646 84,750

2,002 527,024 459,443 415,842 377,256 38,586 93,021 89,8892,003 567,087 492,230 450,090 407,438 42,652 105,382 98,0732,004 619,954 536,749 473,685 427,288 46,397 131,671 109,1812,005 688,754 589,413 521,302 468,849 52,453 155,907 117,5392,006 785,184 654,083 595,327 538,533 56,794 175,632 135,5322,007 864,602 728,178 656,276 589,429 66,847 204,830 148,4212,008 1,009,164 818,401 726,685 645,203 81,482 260,170 172,7462,009 1,221,248 960,012 883,252 776,233 107,019 285,186 203,985

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Year ygfcfpub ygfcfpvt ystock yxgs ymgs yfc yfcagr

1,975 505 1,718 179 1,475 2,215 15,936 11,405

1,976 632 1,811 189 1,874 2,466 16,589 11,495

1,977 689 1,891 189 2,037 2,474 16,255 10,389

1,978 1,114 2,181 212 2,086 3,053 18,426 11,621

1,979 1,138 2,125 251 2,618 3,547 20,779 13,365

1,980 1,466 2,215 589 2,695 4,374 21,886 13,520

1,981 1,823 2,476 509 3,523 5,357 25,466 15,510

1,982 2,487 2,978 (151) 3,592 5,828 29,037 17,715

1,983 2,941 3,635 52 3,455 7,196 31,584 19,002

1,984 3,139 3,768 344 4,196 7,661 37,004 22,570

1,985 3,629 5,757 798 5,372 9,317 44,016 22,762

1,986 3,909 5,522 1,168 6,506 11,218 52,727 27,136

1,987 4,727 7,098 1,073 7,555 13,132 60,372 30,622

1,988 5,483 7,931 1,823 8,717 16,350 72,214 36,754

1,989 7,902 8,490 3,023 9,897 19,162 84,512 42,571

1,990 7,968 9,034 2,074 10,887 21,820 97,748 50,4701,991 8,683 14,097 2,293 14,226 27,785 113,838 55,368

1,992 10,331 18,945 2,343 23,909 39,321 142,000 65,156

1,993 11,769 25,509 2,375 30,948 47,429 161,772 70,072

1,994 13,380 28,652 2,612 47,548 62,972 187,123 80,589

1,995 15,070 33,300 6,861 53,084 75,850 204,914 85,569

1,996 17,624 38,457 11,936 55,405 88,996 233,456 96,896

1,997 19,392 41,402 10,290 73,853 105,775 262,561 108,785

1,998 22,573 42,802 9,353 68,659 101,949 281,902 112,495

1,999 23,888 41,381 4,792 78,150 101,648 320,580 132,373

2,000 26,436 46,888 18,948 88,360 123,055 355,543 145,131

2,001 18,063 66,687 13,896 99,611 146,757 413,428 155,6252,002 17,439 72,450 3,132 81,492 130,912 430,397 166,090

2,003 14,719 83,354 7,309 77,280 140,522 460,324 172,802

2,004 14,955 94,226 22,490 89,544 158,151 500,699 186,125

2,005 17,213 100,326 38,368 85,958 173,754 548,485 199,368

2,006 17,509 118,023 40,100 87,952 204,828 611,118 211,704

2,007 20,843 127,578 56,409 94,979 227,907 676,210 226,8222,008 25,521 147,225 87,424 98,836 267,290 756,756 254,6692,009 39,829 164,156 81,201 150,764 359,190 882,954 298,876

32


33/74


Year yfcind yfcmin yfcmanf yfcutility yfcconst yfcser yfctrade

1,975 1,303 22 664 34 583 3,228 540

1,976 1,469 23 690 38 718 3,625 603

1,977 1,821 26 736 39 1,020 4,045 636

1,978 2,194 20 794 42 1,338 4,611 707

1,979 2,217 34 848 48 1,287 5,197 724

1,980 2,608 42 936 60 1,570 5,758 889

1,981 3,148 58 1,049 67 1,974 6,808 953

1,982 3,733 66 1,243 82 2,342 7,589 1,068

1,983 4,069 85 1,480 127 2,377 8,513 1,199

1,984 4,661 111 1,816 158 2,576 9,773 1,520

1,985 6,649 193 2,511 184 3,761 15,030 4,561

1,986 8,358 228 3,253 327 4,550 17,720 5,622

1,987 9,555 257 3,740 396 5,162 20,963 6,821

1,988 11,676 317 4,615 441 6,303 24,740 8,118

1,989 13,975 421 4,857 466 8,231 29,285 9,052

1,990 15,871 449 5,956 523 8,943 33,361 10,5071,991 20,362 575 7,894 815 11,078 40,397 12,902

1,992 29,627 795 12,822 1,241 14,769 50,150 16,563

1,993 34,400 921 14,618 1,543 17,318 60,878 19,260

1,994 40,635 990 17,861 2,163 19,621 70,372 22,497

1,995 46,627 1,117 19,555 2,862 23,093 77,778 24,326

1,996 53,499 1,342 22,466 3,598 26,093 88,993 28,317

1,997 60,031 1,495 24,816 4,457 29,263 100,754 30,551

1,998 63,406 1,553 26,987 4,383 30,483 113,897 33,687

1,999 69,916 1,685 30,337 4,632 33,262 127,729 39,313

2,000 78,689 1,815 33,550 5,942 37,382 142,431 42,895

2,001 73,561 1,817 38,409 7,750 25,585 196,268 78,3872,002 77,861 2,149 37,736 9,138 28,838 200,101 71,921

2,003 83,538 2,310 38,826 11,447 30,955 217,205 76,235

2,004 89,408 2,507 41,673 11,974 33,254 242,460 88,161

2,005 97,059 2,748 44,885 12,782 36,644 270,152 88,734

2,006 105,098 3,134 47,840 13,172 40,952 313,528 99,612

2,007 115,529 3,417 52,172 14,841 45,099 355,364 103,726

2,008 126,484 3,857 55,900 15,683 51,044 400,108 120,600

2,009 140,506 4,409 61,820 15,865 58,412 471,140 138,705

33


34/74


Year yfctrans yfcfin yfcsocial yfcfism yfcindtax ymp inv invagr

1,975 690 1,095 903 - 635 16,571 2,223 803

1,976 805 1,171 1,046 - 805 17,394 2443 843

1,977 852 1,412 1,145 - 1,025 17,280 2580 731

1,978 1,093 1,534 1,277 - 1,306 19,732 3295 788

1,979 1,520 1,613 1,340 - 1,436 22,215 3263 812

1,980 1,541 1,833 1,495 - 1,465 23,351 3681 754

1,981 1,889 2,077 1,889 - 1,841 27,307 4299 670

1,982 1,992 2,366 2,163 - 1,951 30,988 5465 853

1,983 2,129 2,594 2,591 - 2,177 33,761 6576 1,168

1,984 2,468 2,937 2,848 - 2,286 39,290 6907 1,196

1,985 2,679 3,987 3,803 425 2,571 46,587 9386 1,836

1,986 3,088 4,728 4,282 487 3,007 55,734 9431 1,981

1,987 3,600 5,669 4,873 768 3,492 63,864 11825 2,205

1,988 4,250 6,681 5,691 956 4,692 76,906 13414 2,233

1,989 4,732 8,032 7,469 1,319 4,758 89,270 16392 3,439

1,990 5,724 9,269 7,861 1,954 5,668 103,416 17002 3,2621,991 6,560 10,944 9,991 2,289 6,532 120,370 22780 4,673

1,992 8,558 13,241 11,788 2,933 7,487 149,487 29276 5,776

1,993 10,819 15,684 15,115 3,578 9,702 171,474 37278 6,319

1,994 12,625 18,122 17,128 4,473 12,149 199,272 42032 6,997

1,995 13,995 20,533 18,924 5,060 14,261 219,175 48370 8,281

1,996 15,898 23,521 21,257 5,932 15,457 248,913 56081 10,319

1,997 19,315 27,157 23,731 7,009 17,952 280,513 60794 11,644

1,998 22,598 29,778 27,834 7,896 18,943 300,845 65375 12,560

1,999 24,631 33,203 30,582 9,438 21,456 342,036 65269 11,910

2,000 29,336 36,919 33,281 10,708 23,945 379,488 73324 14,759

2,001 31,425 46,722 39,734 12,026 28,090 441,518 84750 15,5412,002 34,959 48,727 44,494 13,655 29,046 459,443 89889 17,781

2,003 39,362 51,112 50,496 13,221 31,906 492,230 98073 20,901

2,004 46,283 53,719 54,297 17,294 36,050 536,749 109181 23,259

2,005 51,336 66,584 63,498 18,094 40,927 589,412 117539 23,695

2,006 61,250 82,021 70,645 19,212 42,966 654,084 135532 33,924

2,007 69,140 99,258 83,240 21,505 51,968 728,178 153337 30,367

2,008 74,869 106,472 98,167 24,505 61,646 818,402 178446 27,3882,009 95,218 119,449 117,768 27,568 77,057 960,011 211039 31,091

34


35/74


Year invind invmin invmanf invutiliy invconst invser invtrade

1,975 209 1 44 119 44 1,212 811,976 249 1 48 143 57 1,351 941,977 265 1 49 138 77 1,583 971,978 350 1 60 189 99 2,158 1161,979 187 1 49 9 127 2,265 1051,980 154 1 47 11 95 2,773 1041,981 292 1 61 94 135 3,337 1361,982 324 1 85 92 146 4,289 1261,983 557 21 177 163 196 4,852 1921,984 1,496 49 278 982 187 4,214 219

1,985 2,957 12 314 1,478 1,153 4,592 237

1,986 2,434 18 332 722 1,362 5,016 434

1,987 3,552 21 507 1,412 1,612 6,068 662

1,988 3,986 53 644 1,011 2,278 7,195 661

1,989 4,002 2 586 1,312 2,102 8,951 812

1,990 4,095 14 642 1,216 2,223 9,645 606

1,991 4,711 16 989 1,525 2,181 13,396 1,082

1,992 6,038 18 1,392 1,699 2,929 17,462 1,1981,993 8,446 22 1,947 2,672 3,804 22,513 2,335

1,994 9,281 24 2,183 2,706 4,368 25,753 2,504

1,995 11,506 31 2,736 3,218 5,521 28,583 3,370

1,996 13,001 35 3,053 3,750 6,163 32,761 4,104

1,997 14,813 40 3,494 4,228 7,050 34,337 4,477

1,998 16,083 51 3,801 4,562 7,669 36,732 4,870

1,999 17,357 58 3,628 4,892 8,779 36,002 4,790

2,000 18,751 61 4,438 5,299 8,953 39,814 5,686

2,001 17,993 56 4,158 5,393 8,386 51,216 5,327

2,002 16,118 47 3,969 6,244 5,858 55,990 9,425

2,003 18,530 87 4,063 5,328 9,052 58,642 4,9322,004 19,945 54 4,351 5,735 9,805 65,977 6,3172,005 24,420 98 4,664 8,923 10,735 69,424 5,5932,006 24,304 102 4,991 5,783 13,428 77,304 8,9562,007 33,006 67 4,769 7,053 21,116 89,964 7,3712,008 34,596 83 5,717 7,930 20,865 116,462 9,5332,009 41,733 99 6,766 8,031 26,837 138,216 11,293

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36/74


37/74


Year agfert emp empagr empind empmin empmanf emputility

1,975 12,658 5,899 5,570 92 1 82 3

1,976 12,266 6,094 5,751 89 1 78 4

1,977 14,884 6,298 5,938 87 1 75 6

1,978 17,467 6,510 6,131 86 1 72 8

1,979 18,543 6,731 6,331 87 1 69 12

1,980 20,964 6,963 6,537 89 1 66 18

1,981 22,458 7,207 6,749 95 1 63 27

1,982 23,817 7,226 6,718 107 1 75 24

1,983 31,279 7,253 6,687 123 1 90 22

1,984 37,299 7,290 6,657 143 2 108 20

1,985 42,829 7,338 6,626 169 2 129 18

1,986 43,408 7,399 6,595 202 2 154 17

1,987 45,051 7,478 6,565 246 2 184 15

1,988 54,181 7,578 6,535 302 2 220 14

1,989 56,839 7,705 6,505 377 3 263 13

1,990 67,286 7,867 6,475 476 3 314 111,991 72,719 8,076 6,445 611 3 376 10

1,992 84,391 8,347 6,416 793 3 449 10

1,993 83,331 8,519 6,541 813 4 465 11

1,994 73,812 8,697 6,668 834 5 481 13

1,995 90,263 8,880 6,798 857 5 498 16

1,996 70,154 9,068 6,930 880 6 516 19

1,997 64,150 9,262 7,065 905 7 534 22

1,998 47,010 9,462 7,203 931 8 553 26

1,999 45,669 9,662 7,341 957 9 572 30

2,000 37,250 9,868 7,481 985 10 591 35

2,001 23,623 10,081 7,624 1,014 12 611 402,002 19,713 10,300 7,770 1,044 13 632 46

2,003 38,950 10,526 7,918 1,077 15 654 53

2,004 11,711 10,760 8,069 1,112 17 676 61

2,005 18,458 11,002 8,224 1,149 19 699 71

2,006 8,136 11,251 8,381 1,188 21 723 82

2,007 12,751 11,510 8,541 1,231 24 748 94

2,008 3,285 11,778 8,704 1,276 27 773 109

2,009 12,750 12,056 8,870 1,325 30 799 126

37


38/74


Year empconst empser emptrade emptrans empfin empsocial

emptotal

1,975 6 237 98 9 8 121 5,899

1,976 6 254 104 9 9 133 6,094

1,977 5 273 109 9 10 145 6,298

1,978 5 293 115 9 10 159 6,5101,979 5 314 121 8 11 174 6,731

1,980 4 338 127 8 12 190 6,963

1,981 4 363 134 8 13 208 7,207

1,982 6 401 147 10 14 230 7,226

1,983 9 443 161 13 16 254 7,253

1,984 13 491 177 16 17 281 7,290

1,985 20 543 194 20 18 310 7,338

1,986 29 602 213 26 20 343 7,399

1,987 44 667 234 33 22 379 7,478

1,988 66 741 257 41 23 419 7,578

1,989 99 823 282 53 25 463 7,7051,990 148 916 309 67 28 512 7,867

1,991 221 1,020 339 85 30 566 8,076

1,992 331 1,138 373 108 32 626 8,347

1,993 333 1,165 394 112 35 625 8,519

1,994 335 1,194 417 116 38 623 8,697

1,995 337 1,225 441 121 41 622 8,880

1,996 340 1,257 466 126 44 621 9,068

1,997 342 1,292 493 131 47 620 9,262

1,998 344 1,328 522 136 51 619 9,462

1,999 346 1,364 551 141 55 618 9,6622,000 348 1,403 581 147 59 617 9,868

2,001 351 1,443 612 152 63 616 10,0812,002 353 1,486 646 158 68 615 10,3002,003 355 1,531 681 164 72 613 10,5262,004 358 1,579 718 170 78 612 10,7602,005 360 1,629 758 177 83 611 11,0022,006 362 1,683 799 184 89 610 11,2512,007 365 1,739 843 191 96 609 11,5102,008 367 1,798 889 198 103 608 11,7782,009 369 1,861 938 206 111 607 12,056

38


39/74


Year gfexp gfcrexp gfcapexp gfamort gfreceipt gfrev gftax

1,975 1,514 530 969 15 1,291 1,008 842

1,976 1,913 654 1,243 16 1,476 1,116 911

1,977 2,331 784 1,507 39 1,715 1,323 1,100

1,978 2,675 815 1,816 44 2,049 1,582 1,2441,979 3,021 982 1,982 57 2,411 1,812 1,477

1,980 3,471 1,056 2,320 95 2,686 1,880 1,529

1,981 4,092 1,264 2,742 86 3,288 2,419 2,036

1,982 5,361 1,523 3,735 104 3,673 2,680 2,211

1,983 6,979 1,900 4,985 94 3,932 2,842 2,421

1,984 7,437 2,107 5,164 167 4,286 3,409 2,737

1,985 8,395 2,720 5,500 175 4,840 3,917 3,151

1,986 9,797 3,232 6,222 343 5,817 4,645 3,659

1,987 11,513 3,776 7,387 351 7,260 5,975 4,372

1,988 14,105 4,277 9,431 397 9,427 7,350 5,753

1,989 18,005 5,133 12,338 534 9,461 7,781 6,287

1,990 19,669 5,863 13,004 802 11,288 9,313 7,284

1,991 23,550 6,821 15,990 739 12,894 10,730 8,176

1,992 26,418 8,695 16,516 1,207 15,157 13,513 9,876

1,993 30,898 9,862 19,438 1,598 18,942 15,148 11,663

1,994 33,597 10,495 21,204 1,898 21,975 19,581 15,372

1,995 39,060 16,607 19,800 2,653 28,542 24,605 19,660

1,996 46,542 18,697 24,998 2,848 32,718 27,893 21,668

1,997 50,724 20,696 26,574 3,453 36,362 30,374 24,424

1,998 56,118 23,218 28,969 3,931 38,341 32,938 25,940

1,999 59,579 26,390 28,546 4,643 41,588 37,251 28,753

2,000 66,273 29,271 31,788 5,213 48,605 42,894 33,152

2,001 79,835 37,069 37,076 5,691 55,647 48,894 38,8652,002 80,072 48,864 24,773 6,435 57,132 50,446 39,331

2,003 84,006 52,091 22,356 9,560 67,569 56,230 42,5872,004 89,443 55,552 23,096 10,795 73,614 62,331 48,1732,005 102,560 61,686 27,341 13,533 84,514 70,123 54,1052,006 110,889 67,018 29,607 14,265 86,110 72,282 57,4302,007 133,605 77,122 39,730 16,752 103,513 87,712 71,1272,008 161,313 91,410 53,516 16,387 127,943 107,623 85,1562,009 213,578 122,080 73,310 18,189 176,782 142,211 116,997

39


40/74


Year gfnontax gfgrants gfbalance gffloan gfdloan gfcashbal gfindtax

1,975 167 283 (223) 104 100 (18) 6681,976 205 360 (437) 146 200 (91) 6831,977 223 393 (615) 164 300 (151) 8271,978 338 467 (626) 382 240 (4) 964

1,979 335 600 (609) 390 200 (19) 1,2591,980 351 806 (785) 535 180 (70) 1,2971,981 384 869 (804) 693 250 139 1,7051,982 468 993 (1,688) 730 500 (459) 1,8491,983 421 1,090 (3,048) 986 1,000 (1,062) 2,007

1,984 672 877 (3,151) 1,671 1,577 96 2,234

1,985 766 923 (3,555) 1,755 1,800 0 2,6311,986 985 1,173 (3,980) 2,501 1,403 (75) 3,051

1,987 1,603 1,285 (4,253) 2,706 1,645 97 3,651

1,988 1,598 2,077 (4,678) 3,816 1,137 275 4,812

1,989 1,494 1,681 (8,544) 5,666 1,330 (1,547) 5,025

1,990 2,029 1,975 (8,381) 5,960 2,150 (272) 5,914

1,991 2,553 2,165 (10,656) 6,257 4,553 154 5,892

1,992 3,637 1,644 (11,262) 6,817 2,079 (2,366) 8,389

1,993 3,486 3,793 (11,956) 6,921 1,620 (3,415) 9,783

1,994 4,209 2,394 (11,623) 9,164 1,820 (639) 12,714

1,995 4,945 3,937 (10,518) 7,312 1,900 (1,306) 15,863

1,996 6,225 4,825 (13,824) 9,464 2,200 (2,160) 17,083

1,997 5,949 5,988 (14,362) 9,044 3,000 (2,318) 19,191

1,998 6,998 5,403 (17,778) 11,055 3,400 (3,323) 19,911

1,999 8,498 4,337 (17,991) 11,852 4,710 (1,429) 21,4562,000 9,742 5,712 (17,667) 11,812 5,500 (355) 24,5972,001 10,029 6,753 (24,188) 12,044 7,000 (5,144) 28,666

2,002 11,115 6,686 (22,941) 7,699 8,000 (7,242) 28,733

2,003 13,643 11,339 (16,437) 4,546 8,880 (3,011) 32,4812,004 14,158 11,283 (15,828) 7,629 5,608 (2,591) 36,2742,005 16,018 14,391 (18,046) 9,266 8,938 158 41,0442,006 14,852 13,828 (24,780) 8,214 11,834 (4,731) 43,4662,007 16,586 15,801 30,092) 10,054 17,892 (2,146) 52,1892,008 22,467 20,321 (33,370) 8,980 20,496 (3,893) 62,0762,009 25,215 34,570 (36,797) 10,405 25,000 (1,391) 82,820

40


41/74


Year gfcustom gfimpduty gfexpduty gfexrefund gfcusother gfgoodstax gfexcise

1,975 329 182 31 108 8 339 120

1,976 359 205 38 112 4 324 131

1,977 386 216 48 117 6 440 168

1,978 459 334 39 85 1 506 167

1,979 627 536 54 36 1 632 193

1,980 608 505 63 40 1 689 215

1,981 816 685 70 58 3 890 242

1,982 825 740 42 40 3 1,024 304

1,983 761 715 25 20 1 1,246 364

1,984 826 746 30 49 0 1,408 432

1,985 1,065 908 56 100 1 1,566 483

1,986 1,231 1,081 73 76 1 1,820 559

1,987 1,505 1,285 79 138 2 2,146 679

1,988 2,215 1,984 108 121 1 2,597 825

1,989 2,290 2,134 63 92 2 2,735 873

1,990 2,685 2,646 33 - 6 3,229 1,0721,991 3,044 2,753 79 212 2 2,847 1,200

1,992 3,359 2,795 115 447 2 5,030 1,419

1,993 3,945 3,178 141 624 3 5,838 1,453

1,994 5,255 4,356 427 460 12 7,459 1,592

1,995 7,018 5,840 333 838 8 8,845 1,658

1,996 7,327 6,247 150 900 31 9,755 1,946

1,997 8,309 7,093 168 1,009 39 10,881 2,302

1,998 8,502 7,019 217 1,102 164 11,409 2,887

1,999 9,518 7,698 378 1,206 235 11,939 2,953

2,000 10,813 8,960 433 1,332 89 13,784 3,133

2,001 12,552 10,392 493 1,456 211 16,114 3,8052,002 12,659 9,678 917 1,701 362 16,074 3,973

2,003 14,236 10,568 856 2,371 443 18,245 4,7782,004 15,555 10,667 527 3,883 478 20,719 6,2212,005 15,702 12,299 698 2,188 516 25,342 6,4462,006 15,344 11,745 626 2,314 659 28,122 6,5062,007 16,708 13,626 709 1,897 476 35,481 9,2442,008 21,062 17,128 446 2,997 492 41,014 11,2292,009 26,641 21,403 775 3,812 651 56,179 16,293

41


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43/74


Year bopexp bopimp bopgb bopservice bopsercr bopserdb bopincome

1,975 890 1,815 (925) 281 585 304 -

1,976 1,209 2,022 (813) 220 665 444 641,977 1,189 2,045 (856) 420 848 429 741,978 1,065 2,516 (1,451) 484 1,021 537 87

1,979 1,304 2,912 (1,609) 679 1,314 635 1001,980 1,166 3,569 (2,403) 723 1,529 806 1501,981 1,613 4,443 (2,830) 996 1,910 914 1211,982 1,496 4,948 (3,452) 1,216 2,096 879 1621,983 1,136 6,333 (5,197) 1,635 2,319 684 -1,984 1,710 6,534 (4,824) 1,358 2,486 1,128 491,985 2,746 7,769 (5,022) 1,077 2,626 1,548 8441,986 3,086 9,372 (6,286) 1,575 3,420 1,845 (0)1,987 3,003 10,927 (7,924) 2,348 4,552 2,204 (20)1,988 4,127 13,893 (9,766) 2,398 4,589 2,191 (187)1,989 4,211 16,297 (12,086) 2,821 5,686 2,865 1681,990 5,170 18,357 (13,187) 2,245 5,681 3,436 5081,991 7,403 23,256 (15,852) 2,294 6,823 4,529 3981,992 13,726 31,987 (18,261) 3,300 10,633 7,333 593

1,993 17,286 39,260 (21,974) 4,692 13,424 8,732 3731,994 19,316 51,629 (32,313) 16,888 28,232 11,344 589

1,995 17,680 63,740 (46,060) 23,293 35,403 12,110 272

1,996 19,913 74,571 (54,658) 21,067 35,493 14,426 (144)

1,997 22,663 93,662 (70,999) 39,078 51,190 12,113 283

1,998 27,540 89,154 (61,614) 28,323 41,119 12,796 805

1,999 35,693 87,695 (52,002) 28,504 42,457 13,953 1,698

2,000 58,495 110,750 (52,256) 11,925 30,820 18,895 2,361

2,001 69,789 126,238 (56,450) 9,302 29,822 20,519 1,701

2,002 57,984 111,342 (53,359) 3,938 23,508 19,570 (605)

2,003 50,761 121,053 (70,292) 7,050 26,519 19,469 (676)

2,004 55,228 132,910 (77,682) 9,075 34,316 25,241 (1,684)

2,

Tarun Das ADB Nepal Upgraded Macro Economic Model Part-2

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Transcript of Tarun Das ADB Nepal Upgraded Macro Economic Model Part-2