Analysis of Macroprudential, Monetary and Fiscal Policy ...

Analysis of Macroprudential, Monetary and Fiscal

Policy Interventions to Maintain the Economic

Stability in Indonesia

Hari Setia Putra1, Jemi Juneldi2

1,2 Universitas Negeri Padang, Padang, Indonesia

Corresponding author. Email: [email protected]

ABSTRACT

This study aims to analyze intervention of macroprudential, monetary and fiscal policy to maintain the

economic stability in Indonesia. The data is used in monthly form from January 2012 to December 2019.

This study uses the Vector Error Correction Model (VECM) because in the VAR model there is

cointegration which indicates a long-term balance. The research process includes stationarity test,

determination of Optimal Lag, Cointegration Test with Johansen test, Granger Causality Test, making

VECM equations, analysis on Impulse response function (IRF) and Forecast Error Variance Decomposition

(FEVD) analysis. The results showed a long-term balance between the variables analyzed, relationships

among variables as well as the response of each variable when there was a change in other variables so

that an optimal combination of policies was needed to maintain economic stability.

Keywords: macroprudential, economic stability, vecm

1. INTRODUCTION

Economic stability is the ultimate goal of

various policies issued by the government.

Economic stability includes how to create

stability in the exchange rate, inflation and

output or GDP. The government is trying to keep

the value of each of these variables in a certain

range. The government through a variety of

policies seeks to create economic stability

through its intervention, both affecting these

variables directly or through intermediaries. Each

country has a diverse mix of policies to maintain

that stability.Stability on the exchange rate plays

an important role for various aspects of the

economy and has a systemic and broad

influence.The research conducted by (Gaies et al.,

2019)shows that the crisis that occurred in banks

declined when there was stability in the

exchange rate.For the another aspect, research by

(Blau, 2018)gave the conclusion that the high

volatility in the exchange rate conduct the price

of stocks became instable. Resarch by (Samii &

Clemenz, 1988)states that fluctuations in the

foreign exchange market have become an

important destabilizing factor for the oil market.

Price stability reflected in inflation affects

people's purchasing power and business

expansion by the company. According to

research conducted by(Gomis-Porqueras et al.,

2020)the intensive negative marginal impact of

inflation on capital demand on companies.

Furthermore, research conducted by(Li et al.,

2017)states that inflation has a significant effect

on asset allocation and consumption choices

made by the public.In addition, research carried

out by (Paradiso et al., 2012)shows that inflation

in the long run has a relationship with

consumption, labor income, wealth over share

and non-share ownership.Output or GDP

produced by a country comprehensively

Advances in Economics, Business and Management Research, volume 152

Proceedings of the 5th Padang International Conference On Economics Education, Economics, Business and

Management, Accounting and Entrepreneurship (PICEEBA-5 2020)

Copyright © 2020 The Authors. Published by Atlantis Press SARL.This is an open access article distributed under the CC BY-NC 4.0 license -http://creativecommons.org/licenses/by-nc/4.0/. 153

mailto:[email protected]

measures the economic health of a country.

According to(Costanza, 2009),are a number of

ways to measure national-level progress in terms

of a measure of economic quantity. GDP alone

can be used to compare economic performance of

countries, but very often these comparisons are

expanded to evaluate and make estimates of

living standards, progress or social welfare

between countries(Tjukanov, 2011)Each country

seeks to create an increase in GDP in a

sustainable and stable manner, this is a picture of

the country's economy.

To maintain stability in the exchange rate,

inflation and GDP, several policy mixes are

needed, including monetary and fiscal policies.

However, there are also macroprudial policies

that are used as an instrument to directly affect

stability. Mix or combination of policies is very

important for various countries today because

there are almost no countries that are closed.

Each country has relations with other countries

in international scope. This causes the

combination of various policies needed to create

economic stability.Research conducted by(S. Kim

& Lim, 2018)shows that contractionary monetary

policy can encourage exchange rate appreciation.

Research conducted by(Okimoto, 2018)shows the

strong relationship between inflation trends and

monetary policy regimes. Looking at the

relationship between monetary policy and GDP,

research conducted by(Lee & Werner,

2018)shows that there is a relationship between

interest rates which constitute monetary policy

and economic growth and nominal GDP growth

provides information about future interest rates

better than interest rates tell about future GDP

growth. Fiscal policy through government

expenditure can encourage the expansion of

private sector activities(Kuismanen & Kämppi,

2010). According to (Boiciuc, 2015), Recent

economic recession, fiscal policy is considered

more attractive because it is expected to be

effective in economic recovery and given the

limited scope of monetary policy to provide

additional stimulus, fiscal policy has become the

most important tool to stabilize the business

cycle. In addition to monetary and fiscal policies,

governments in various countries have also

begun to make macroprudential policies as an

alternative in maintaining economic stability.

Based on research results(J. Kim et al., 2019)

shows that macroprudential policy affects credit

and output and its impact is almost similar to

monetary policy.

Indonesia as the developing country has a

high vulnerability to economic stability.

Economic condition in developing countries are

greatly affected by various factors, bothc

domestic and foreign. Instability economic has

large leverage on peoples live, especially social

welfare. In consequence, it needs policy from

government, both to maintain economic stability

and to recovery economic conditions from

instability. The diversity of policies carried out

by governments in various countries of the world

is one of the attractions in conducting research.

This study seeks to see what policies are most

dominant in influencing economic stability in

Indonesia, which is seen from three variables,

namely exchange rate stability, inflation and

GDP. Different conditions in a country will give

different results to the various implementation of

policies. So this research will look at what

policies most dominantly affect economic

stability in Indonesia and how much the

contribution of each policy to these variables. In

addition, it also looks at how the most effective

combination of policies in maintaining economic

stability.

2. METHODS

Data uses in montly from January 2012 to

December 2020. Variables used consist of reserve

requirement as the macroprudential policy

indicator which means deposit or reserve of

Banks in central bank and this data obtained

from Bank Indonesia. BI Rate as monetary policy

indicator and this data obtained from Bank

Indonesia. Government expenditure as fiscal

policy indicator and this data collected from

Indonesian Ministry of Finance. Indicators used

to measure economic stability include exchange


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rate Rupiah per US Dollar which data obtained

from investing.com, real GDP and inflation that

measure by consumer price index. Data of GDP

and Inflation obtained from Indonesian’s Central

Bureau of Statistic (BPS). Data of government

expenditure and GDP are interpolated to convert

into monthly form. Analysis method in this

research used Vector Error Correction Model

(VECM) with Eviews program. VECM is an

analysis tool used if a set of variables is found to

have one or more cointegration vectors that

adjust both short-term changes in variables and

deviations from equilibrium(Maria & Andrei,

2016). The first step taken is to test the

stationarity of each variable. VECM requires that

all variables must be stationary at the same level.

Stationarity test was carried out with the

Augmented-Dickey Fuller (ADF) Test. To see

whether the variable is stationary or not,

compare the probability value with alpha 0.05. If

the probability is smaller than alpha 0.05, it can

be said that the variable is stationary. The next

step is optimal Lag testing. Determination of the

optimal lag is one of the important things

because it is useful to eliminate the problem of

autocorrelation in the VAR system that is used as

a VAR stability analysis(AGUS TRI BASUKI,

2016). The next step is testing the stability of

VAR. This test is carried out whether IRF and

FEVD analysis. When the modulus value is

smaller than one in the Roots of Characteristic

Polynomial is smaller than 1, the VAR model is

said to be stable and the results of IRF and FEVD

become valid. The next step is cointegration test.

The concept of cointegration is basically to see

the long-term balance between the observed

variables(Agus Suharsono, Auliya Aziza, 2017).

Cointegration test is done by Johansen Test. The

Trace Statistics value is compared with the

Critical Value at 0.05 at none * and the Max-Eigen

Statistics value with a Critical Value at 0.05 at

none *. If the Trace Statistics value of the Critical

Value is 0.05 at none * and the Max-Eigen

Statistics value is greater than the Critical Value

of 0.05 at none * at a small probability of alpha

0.05, then it can be said that there is cointegration

in the model or a long-term balance. The next

step is the Granger Causality Test. This test is

done to see whether there is a reciprocal

relationship between the variables used. The next

step is to make a VECM estimate. The next step is

the analysis of the Impulse Response Function

which is used to determine the response of an

endogenous variable to a certain variable

shock(AGUS TRI BASUKI, 2016). The final step is

the analysis of Forecast Error Variance

Decomposition (FEVD). This analysis aims to see

how much the contribution of one variable to

changes in other variables in a certain period.

Model equations of this research:

1. BI Rate=a10+a11BIRatet-1 +a12INFt-

1+a13LERt-1+a14LG+a15LGDPt-1+a16LRRt-

1+e

2. INF=a21+a21INFt-1 +a22BIRatet-1+a23LERt-

1+a24LG+a25LGDPt-1+a26LRRt-1+e

3. LER=a31+a31LERt-1 +a32INFt-1+a33INFt-

1+a34LG+a35LGDPt-1+a36LRRt-1+e

4. LG=a41+a41LGt-1 +a42LERt-1+a43INFt-

1+a44BIRate+a45LGDPt-1+a46LRRt-1+e

5. LGDP=a51+a51LGDPt-1 +a52LGt-

1+a53LERt-1+a54INF+a55BIRatet-

1+a56LRRt-1+e

6. LRR=a61+a61LRRt-1 +a62LGDPt-1+a63LGt-

1+a64LER+a65INFt-1+aBIRatet-1+e

Where:

BI Rate : Interest Rate of Central Bank

(Bank Indonesia)

INF : Inflation

LER : Log Exchange rate

LG : Log Government Expenditure

LGDP : Log GDP

LRR : Log Reserve Requirement

3. RESULT AND DISCUSSION

Stationarity Test

To see the stationary conditions, then use the

ADF Test by comparing the probability of each

test with alpha 5% or 0.05. If the probability

value is smaller than alpha 0.05, then the data

used is stationary at that level. From the test

results with the ADF Test, it can be concluded

that only the LRR variable is stationary at the


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level of the level and the rest is stationary at the

level of the 1st Difference. So in the analysis of

this study using stationary data at the level of the

1st Difference.

Table 1. ADF Test

Variables Prob. Level Prob. 1st Difference

BIRate 0.5109 0.0000

INF 0.2595 0.0000

LER 0.1852 0.0000

LG 0.394864 0.0000

LGDP 0.3072 0.0043

LRR 0.0155 0.0000 Source: Author's processed results

Optimum Lag Length Test

Table 2. Lag Length Criteria

Lag LogL LR FPE AIC SC HQ

0 765.4721 NA 1.05e-15 -17.45913 -17.28907* -17.39065

1 832.5139 123.2952 5.17e-16 -18.17273 -16.98229 -17.69338

2 858.9311 44.93961 6.52e-16 -17.95244 -15.74162 -17.06221

3 934.8762 118.7187 2.68e-16 -18.87072 -15.63953 -17.56962

4 975.4605 57.84441 2.55e-16 -18.97610 -14.72454 -17.26413

5 1005.405 38.54936 3.23e-16 -18.83690 -13.56496 -16.71405

6 1127.295 140.1032 5.21e-17 -20.81138 -14.51906 -18.27766*

7 1164.166 37.29513 6.39e-17 -20.83141 -13.51872 -17.88681

8 1229.891 57.41439* 4.45e-17* -21.51473* -13.18166 -18.15926

Source: Author's processed results

By using Akaike Information Criterion which

has the smallest value or has an asterisk (*), then

the optimal lag used in this analysis is Lag 8

because it has the smallest AIC value with a

value of -21.51473. So henceforth will use lag 8 in

the analysis in this study. This test is notable to

omitting an autocorrelation problem in VAR

system which was used in VAR stability analysis.

VAR Model Stability Test

Table 3. AR Root Table

Root Modulus

0.700337 0.700337

0.490992 - 0.293021i 0.571782

0.490992 + 0.293021i 0.571782

0.065937 - 0.524464i 0.528593

0.065937 + 0.524464i 0.528593

0.208205 - 0.420707i 0.469408

0.208205 + 0.420707i 0.469408


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-0.227188 - 0.323611i 0.395397

-0.227188 + 0.323611i 0.395397

-0.278855 - 0.140677i 0.312330

-0.278855 + 0.140677i 0.312330

-0.031676 0.031676


To see if the VAR model is stable, then

compare the modulus value on the AR Root

Table results with 1. If it is greater than 1, the

system used is unstable and if it is smaller than 1,

it can be said that the system used is stable. These

results indicate whether the IRF and FEVD

analyzes are valid or not. When the system used

is stable, it can be concluded that the IRF and

FEVD analysis are valid. From table result above,

it can be concluded that sytem was used is stable

because all value of modulus smaller than 1.

Cointegration Test

Table 4. Cointegration Test Result

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.788688 279.2979 95.75366 0.0000

At most 1 * 0.371876 136.2911 69.81889 0.0000

At most 2 * 0.289469 93.50953 47.85613 0.0000

At most 3 * 0.238681 62.06925 29.79707 0.0000

At most 4 * 0.214409 36.98062 15.49471 0.0000

At most 5 * 0.148405 14.77928 3.841466 0.0001


To see whether or not there is cointegration in

the analysis equation, it is seen by using the

Johansen Trace Statistics Test. The Trace Statistics

value is compared with the Critical Value of 0.05

at None * and the Max-Eigen Statistics value with

a Critical Value of 0.05 at None *. If the Trace

Statistics value is greater than the Critical Value

0.05 in None * and the Max-Eigen Statistics value

is greater than the Critical Value 0.05 in none *,

then it can be said that in the model there is a

cointegration that is the condition of the long-

term balance of the variables used. Based on the

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05

No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None * 0.788688 143.0067 40.07757 0.0001

At most 1 * 0.371876 42.78160 33.87687 0.0034

At most 2 * 0.289469 31.44028 27.58434 0.0152

At most 3 * 0.238681 25.08864 21.13162 0.0131

At most 4 * 0.214409 22.20134 14.26460 0.0023

At most 5 * 0.148405 14.77928 3.841466 0.0001


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processed results, it can be seen that the Trace

Statistics value of 279.2979 is greater than the

Critical Value of 0.05 of 95.75366 in None * with

significant conditions because the probability is

smaller than alpha 0.05 and the Max-Eigen

Statistics value of 143.0067 is greater than the

critical value of 0.05 is 40.07757 at none * with

conditions that are also significant with a small

probability of alpha 0.05.

Granger Causality Test

Table 5. Pairwise Granger Causality Tests

Null Hypothesis: Obs F-Statistic Prob.

INF does not Granger Cause BIRATE 88 1.94547 0.0663

BIRATE does not Granger Cause INF 0.38390 0.9258

LER does not Granger Cause BIRATE 88 0.91083 0.5127

BIRATE does not Granger Cause LER 2.38379 0.0246

LG does not Granger Cause BIRATE 88 1.16812 0.3305

BIRATE does not Granger Cause LG 0.70970 0.6821

LGDP does not Granger Cause BIRATE 88 0.83540 0.5746

BIRATE does not Granger Cause LGDP 0.18552 0.9922

LRR does not Granger Cause BIRATE 88 1.13443 0.3513

BIRATE does not Granger Cause LRR 1.43177 0.1985

LER does not Granger Cause INF 88 1.59242 0.1425

INF does not Granger Cause LER 1.94564 0.0663

LG does not Granger Cause INF 88 0.65955 0.7250

INF does not Granger Cause LG 0.90954 0.5137

LGDP does not Granger Cause INF 88 0.86255 0.5520

INF does not Granger Cause LGDP 0.11244 0.9986

LRR does not Granger Cause INF 88 1.64597 0.1273

INF does not Granger Cause LRR 1.68323 0.1175

LG does not Granger Cause LER 88 2.60307 0.0148

LER does not Granger Cause LG 3.70715 0.0011

LGDP does not Granger Cause LER 88 1.09701 0.3756

LER does not Granger Cause LGDP 0.35960 0.9382

LRR does not Granger Cause LER 88 0.95324 0.4793

LER does not Granger Cause LRR 1.73453 0.1053

LGDP does not Granger Cause LG 88 16.2149 2.E-13

LG does not Granger Cause LGDP 11.8256 2.E-10


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LRR does not Granger Cause LG 88 5.90295 9.E-06

LG does not Granger Cause LRR 3.76109 0.0010

LRR does not Granger Cause LGDP 88 2.31399 0.0288

LGDP does not Granger Cause LRR 1.97117 0.0626


The Pairwise Granger Causality tests table

shows whether there is a reciprocal relationship

between two variables. Based on the table above,

it can be concluded that the BI Rate Variable

significantly affects the Exchange Rate with a

probability of 0.0246 which is smaller than alpha

0.05 and only one-way relationships occur.

Government Expenditure (LG) has a significant

effect on the Exchange Rate and vice versa so that

there is a two-way relationship between the two.

LGDP has a significant effect on Government

Expenditure (LG) and vice versa. Reserve

Requirment (LRR) has a significant effect on

Government Expenditure (LG) and vice versa so

that there is a two-way relationship between the

two variables. Reserve Requirment (LRR) has a

significant effect on LGDP and LGDP has no

significant effect on LRR so that the two have

only one-way relationships.

The result of VECM estimate

Table 6. VECM result estimate

Var. Independent Var. Dependent Coefficient t-Statistic Result

BI Rate (-1) LER 0.033647 2.49174 Significant

BI Rate (-1) LRR 0.011924 2.07648 Significant

BI Rate (-4) LER 0.034808 2.24227 Significant

BI Rate (-5) LG -0.082704 -2.38528 Significant

BI Rate (-6) LER -0.038152 -2.21458 Significant

LER (-1) LG 0.935302 2.67914 Significant

LER (-8) LG 1.046272 2.96474 Significant

LG(-6) LGDP -0.019983 -2.39774 Significant

LRR(-1) LER -0.908890 -2.46703 Significant

LRR(-6) INF 34.96208 2.80278 Significant

LRR(-6) LER 1.217061 3.36775 Significant

LRR(-7) INF 43.21895 2.82741 Significant Source: Author's processed results

To analysis the VECM, it compared between

t-statistc values and t-table value. If the value of

the t-statistic is bigger than the value of the t-

table, that can be concluded significant affects of

the independent variable to the dependent

variable and the coefficient value showed how

much the contribution of independent variable’s

alteration to the value of dependent variable.

Based on measurement of the amount of data

and variables that used in this research, the value

of t-table is 1.986 for the significantly at alpha

0.05. Table 7 showed all of variables that have

significant affect to the other variables.

BI Rate in one period (month) previously, has

significant affect toLER dan LRR in current

period and the coefficient value showed the

positive affect. Positive relationship between BI

rate in one period previously on the LER in

current period showed if there is increasing in BI

Rate will make depreciation Rupiah/US Dollar at

present. This condition almost similar with result

of the research by (Saraç & Karagöz, 2016), if the


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interest rate in short-run is higher, it causes

depreciation in exchange rate.The reason of this

statement is because increasing in interest rate

causes the loan of cost became higher, causes

bankruptcy, weakes the banking system, worsen

the financial condition and capital flight. The

positive affect of the BI Rate to LRR have

meaning that the increasing of interest rate

causes the reserve of banks in central bank will

increase. When Bank Indonesia increases the BI

Rate in one period previously, one of the aim is

to decrease the inflation with reduces the money

supply and push banks to enhance their reserve

in Bank Indonesia.

BI Rate in four and six periods (months)

previously, have significant and negative affect

to the current LER. It is have meaning that the

increasing of BI Rate in four and six periods

previously, it responds byapreciation of

exchange rate Rp/US Dollar in current

period.This respond is different with BI Rate

policy in one period previously that have affect

on depreciation of exchange rate.This condition

almost similar from the research of(Partachi &

Mija, 2015) showed that the increasing of interest

rate in domestic causes depreciation of exchange

rate continuously. Another research by (Hacker

et al., 2014)with used granger causality showed

that there is negative correlation between interest

rate and exchange rate, but it is just effective for

short run.

The increasing of BI Rate in five periods

previously has significant and negative affect to

the current LG.This negative affect also showed

by result of (Chang & Tsai, 1998), that a lower

interest rate washappened when the high of the

government expenditure, but is just for

temporary.One of the reasons of this condition is

when interest rate is high, it make government to

reduce its budget to investment.

LER on one and eight periods (months)

previously, have significant and positive affect to

the current LG. Depreciation of exchange rate ini

one and eight periods previously cause the

increasing of government expenditure currently.

This is because government will adjust its budget

to maintain the exchange rate. This condition

almost similar with result of the research by

(Miyamoto et al., 2019),increasing of government

expenditure causes the apreciation of exchange

rate. From this result, the respond of fiscal policy

throughgovernmentt budget to surmount

exchange rate depreciation is through increasing

of government expenditure. LG on the six period

previously has significant and negative affect to

current GDP. When the government expenditure

increases in six periods previously, it decreases

the value of GDP currently. This condition

almost similar with the result of research by

(Hasnul, 2015)that increasing of government

expenditure reduces the economic growth and

also growth of GDP, this is because the

increasing of government expenditure causes the

tax-cost is higher or cost of loan is higher and

amount of debt and interest are higher.

LRR has different influence LER at some

periods. LER on one period previously has

significant and negative affect to LER, but on six

periods previously has significant and positive

affect to LER. Increasing of banks reserve

requirement at central bank in one period

previously, it causes appreciation exchange rate.

Through this way, it decreases the money supply

and push apreciation of exchange rate. Based on

research (Glocker & Pascal, 2012), reserve

requirement is one of the potential way to creates

the apreciation of exchange rate. But, this

condition is not same with six periods previously

that the increasing of reserve requirement banks

in bank central causes the apreciation of

exchange rate.

LRR on six and seven periods (months)

previously, have significant and positive affect to

INF. Increasing of reserve requirement banks in

six and seven periods previously, that impact to

the increasing of inflation currently. Positive

correlation also showed by the research of

(Glocker & Pascal, 2012),positive shock on

reserve requirement causes increasing of

inflation. Increasing of reserve requirement

contributes to decreasing of money supply


160

because banks deposit more money in central

bank and reduce their credit on household and

firm. But, in this research shows different result

and conclusion about the impact of reserve

requirement to inflation compare to the theory.

IRF Analysis

Table 8. Response of BI Rate

Period BIRATE INF LER LG LGDP LRR

1 0.207890 0.000000 0.000000 0.000000 0.000000 0.000000

2 0.296915 0.048016 0.040607 0.037342 0.010423 -0.028892

3 0.378041 0.055159 0.083843 0.058670 0.039159 -0.039032

4 0.425000 0.062861 0.111414 0.042741 0.051393 -0.020910

5 0.434548 0.083624 0.120751 0.050350 0.041565 -0.040792

6 0.463817 0.062943 0.107873 0.040139 0.041965 -0.040919

7 0.512901 0.081704 0.118925 0.013229 0.038939 -0.044392

8 0.550842 0.045033 0.127074 0.024034 0.056166 -0.028614

9 0.543278 0.073047 0.154247 0.032010 0.050882 -0.020035

10 0.547833 0.095604 0.172918 0.052675 0.053410 -0.029218


Table 8 show responses of BI Rate towards

change of other variables. Based on table above,

BI Rate give the biggest response of change in

variable LER than it responds to change of other

variables. This result also similar with VECM

estimate that BI Rate more dominant affects LER.

One of the reason for this statement is Bank

Indonesia has goal to makesure the stability of

Rupiah’s value and one of the measure stability

is exchange rate Rupiah per US dollar. Then to

affect the exchange rate, Bank Indonesia

intervenes through adjustment of interest rate or

BI Rate. So, BI rate become more responsive to

volatility of exchange rate.

Table 9. Response of INF


1 0.074248 0.619661 0.000000 0.000000 0.000000 0.000000

2 0.306908 0.672199 0.041567 -0.098564 0.167411 -0.165778

3 0.412759 0.503569 0.102871 -0.167981 0.145536 -0.148126

4 0.386772 0.391114 -0.062242 -0.129124 0.048337 -0.122357

5 0.406329 0.330227 -0.143967 -0.103843 0.011939 -0.119408

6 0.418928 0.276093 -0.057115 -0.068655 0.032380 -0.104435

7 0.442667 0.374670 0.018949 0.008005 0.057407 0.033808

8 0.453582 0.501680 -0.066634 0.070598 0.112294 0.166284

9 0.336715 0.548899 -0.190422 0.149095 0.121286 0.251098

10 0.313176 0.439265 -0.264157 0.156054 0.037772 0.310637


Table 9 show responses of INF to tha change

of other variables. From table above, INF give

biggest response to the change of BI Rate. This is

because BI rate is a primary instrument of Bank

Indonesia to influence money supply then will

give impact to inflation.


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Table 10. Response of LER


1 0.001308 0.000372 0.018029 0.000000 0.000000 0.000000

2 0.010448 0.004467 0.015971 0.000375 0.005557 -0.006178

3 0.010127 0.009747 0.019285 0.003852 0.005894 -0.007841

4 0.009771 0.006143 0.018045 -0.000637 0.003985 -0.008380

5 0.015319 0.008235 0.018128 -0.001476 0.004717 -0.005060

6 0.018544 0.009054 0.020495 -0.000633 0.008082 -0.004102

7 0.016036 0.007784 0.022144 0.002769 0.006816 -0.001536

8 0.018117 0.003782 0.021265 0.001899 0.004429 -0.003396

9 0.014366 0.002484 0.023064 0.002161 0.004967 0.001810

10 0.012496 0.005539 0.020867 0.004322 0.007641 0.004568


Table 10 show responses of LER to the change

of other variables. From table above, LER give

biggest response to the change of BI Rate. This is

because Bank Indonesia through monetary policy

(BI Rate) have goal to maintain stability of

exchange rate.

Table 11. Response of LG


1 0.006684 0.005861 -0.004927 0.036001 0.000000 0.000000

2 0.009604 0.017686 0.008414 0.036946 0.000112 0.004358

3 0.011162 0.023643 0.000200 0.028677 0.004601 0.011631

4 0.013363 0.026342 0.010398 -0.009089 0.017039 0.022297

5 0.020475 0.009770 -0.004765 -0.015094 0.014698 0.018012

6 0.011983 -0.001059 -0.004996 -0.017037 0.001153 0.011477

7 8.45E-05 -0.026529 -0.019444 -0.003509 -0.011746 0.001558

8 -0.019310 -0.026333 -0.006676 -0.004028 -0.010768 0.005001

9 -0.007754 -0.016744 0.006172 0.004003 -0.002555 -0.005489

10 0.007274 0.012153 0.023747 -0.005687 0.000758 -0.005643


Table 11 show responses of LG to the change

of other variables. From table above, LG give

biggest response to the change of INF. This is

because government through fiscal policy such as

adjustment of government expenditure affects

the output.

Table 12. Response of LGDP


1 -0.000108 0.000265 0.000868 -0.000389 0.002171 0.000000

2 -9.17E-05 0.000211 0.000444 -0.000403 0.001764 -0.000136


162

3 0.000302 2.52E-05 8.77E-05 -0.000296 0.000447 -0.000306

4 0.000561 0.000619 -9.76E-05 -0.000107 0.000841 -0.000374

5 0.000165 0.000334 -6.04E-05 -0.000120 0.001666 2.56E-05

6 0.000369 -0.000109 8.20E-05 0.000352 0.000949 -0.000190

7 0.000807 -0.000289 5.51E-05 7.10E-05 -0.001360 -0.000453

8 0.000627 1.13E-05 0.000230 -0.000389 -0.000161 -2.67E-05

9 -0.000109 0.000111 -0.000134 -0.000365 0.000988 0.000461

10 -4.64E-05 -0.000837 -0.000367 0.000533 -0.000300 1.13E-05


Table 12 show responses of LGDP to the

change of other variables. From table above,

LGDP give biggest response to the change of LG.

This is because LG or government expenditure as

the fiscal policy directly affects output or GDP.

Table 13. Response of LRR


1 -0.001934 0.001449 0.002506 0.001525 0.000737 0.006643

2 0.001806 0.002336 0.002649 0.002083 0.001678 0.003070

3 0.001367 0.003241 0.002176 0.005519 0.000745 0.002667

4 0.001311 0.001634 0.004321 0.002818 0.001408 0.004587

5 0.000546 0.002157 0.003446 0.002800 0.001895 0.005288

6 0.000424 0.002421 0.002524 0.005417 0.002355 0.006915

7 0.000301 0.002111 0.004089 0.006559 0.001380 0.008443

8 0.000184 0.001140 0.004883 0.006730 0.001210 0.007508

9 -0.000333 -0.000190 0.004276 0.007093 0.002253 0.008208

10 -0.002460 0.001652 0.005581 0.007684 0.001694 0.010355


Table 13 show responses of LRR to the change

of other variables. From table above, LRR give

biggest response to the change of variable LG.

This is show that policy of reserve requirement

also responsive towards shock of fiscal policy.

FEVD Analysis

Table 14. Variance Decomposition of BIRATE

Period S.E. BIRATE INF LER LG LGDP LRR

1 0.207890 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000

2 0.371037 95.42945 1.674697 1.197723 1.012886 0.078914 0.606325

3 0.545119 92.30584 1.799760 2.920552 1.627639 0.552596 0.793610

4 0.706435 91.15653 1.863445 4.226365 1.335213 0.858287 0.560164

5 0.845803 89.98664 2.277452 4.986489 1.285817 0.840235 0.623372

6 0.975271 90.29822 2.129453 4.973866 1.136474 0.817107 0.644883

7 1.112970 90.57405 2.174040 4.961019 0.886785 0.749831 0.654274

8 1.250942 91.08632 1.850515 4.958929 0.738871 0.795138 0.570229


163

9 1.375918 90.88137 1.811466 5.355744 0.664865 0.794005 0.492547

10 1.496259 90.25587 1.940055 5.864448 0.686151 0.798836 0.454636


Table 14 show contributions of change othe

variables to the change of BI Rate in percent.

Change on BI Rate has biggest contribution to the

change of its self, it contribution is around 90%.

Another variable that has big contribution to

change of BI Rate is LER, it contribution is

around 4% to the change of BI Rate.

Table 15. Variance Decomposition of INF


1 0.624093 1.415366 98.58463 0.000000 0.000000 0.000000 0.000000

2 1.001243 9.945794 83.37569 0.172352 0.969069 2.795678 2.741413

3 1.228154 17.90523 72.22490 0.816133 2.514807 3.262290 3.276643

4 1.359698 22.69975 67.20016 0.875407 2.953593 2.787981 3.483105

5 1.472699 26.96238 62.31121 1.701864 3.014914 2.383121 3.626509

6 1.562211 31.15230 58.49861 1.646092 2.872453 2.160811 3.669737

7 1.667841 34.37567 56.36985 1.457096 2.522432 2.014247 3.260710

8 1.813504 35.33093 55.33083 1.367430 2.285041 2.087087 3.598679

9 1.959519 33.21443 55.23873 2.115585 2.536119 2.170746 4.724394

10 2.079135 31.77150 53.52925 3.493368 2.816056 1.961164 6.428666


Table 15 show contribution of other variables

to the change of variable INF. Change of variable

BI Rate give biggest contribution to the change of

INF. It contribution is around 24%. Another

variable that give big contribution to the change

of INF is LRR. This is because Bank Indonesia

through macroprudential policy as reserve

requairement can to affects money supply and it

also have relation with inflation.

Table 16. Variance Decomposition of LER


1 0.018081 0.523692 0.042242 99.43407 0.000000 0.000000 0.000000

2 0.027934 14.20951 2.574422 74.34904 0.018011 3.957243 4.891774

3 0.038221 14.61101 7.878772 65.17159 1.025370 4.491796 6.821460

4 0.044790 15.39837 7.618185 63.68621 0.766855 4.062446 8.467933

5 0.051839 20.22751 8.210700 59.77322 0.653527 3.860707 7.274341

6 0.060131 24.54395 8.369406 56.04214 0.496805 4.675874 5.871828

7 0.066935 25.54723 8.106814 56.17212 0.572112 4.810382 4.791348

8 0.072869 27.73772 7.109777 55.91346 0.550620 4.428327 4.260098

9 0.078019 27.58692 6.303401 57.51435 0.557043 4.268224 3.770055

10 0.082505 26.96222 6.087180 57.82602 0.772516 4.674338 3.677731



164

Table 16 show contributions of other variables

to the change of LER. Variable BI Rate have

biggest contribution to the change of LER. It

contribution is around 19% of the change in

exchange rate. This result also similar with

conclusion in the VECM estimate and IRF

analysis. BI Rate has biggest contribution because

Bank Indonesia efforts to maintain the Rupiah’s

value by intervening in foreign exchange market

and it through the monetary policy such as BI

Rate.

Table 17. Variance Decomposition of LG


1 0.037408 3.192903 2.454761 1.734834 92.61750 0.000000 0.000000

2 0.057090 4.200803 10.65118 2.917212 81.64762 0.000386 0.582807

3 0.070155 5.313159 18.41129 1.932645 70.77793 0.430448 3.134530

4 0.082294 6.498109 23.62583 3.000999 52.65670 4.599522 9.618837

5 0.089878 10.63769 20.98864 2.797065 46.96593 6.530307 12.08037

6 0.093118 11.56616 19.56627 2.893667 47.10147 6.099072 12.77336

7 0.099527 10.12472 24.23247 6.349720 41.35542 6.731773 11.20589

8 0.105706 12.31293 27.68824 6.027965 36.80736 7.005527 10.15798

9 0.107726 12.37340 29.07525 6.132217 35.57757 6.801450 10.04011

10 0.111509 11.97375 28.32398 10.25844 33.46483 6.352446 9.626557

Source:

Author

's

process

ed

results



to the change of LG. Except its self, inflation has

biggest contribution to the change of government

expenditure and it is around 20%. Variable BI

Rate has contribution around 8%, reserve

requirement around 8%, exchange rate around

4,4% and GDP has contribution around 4%.

Table 18. Variance Decomposition of LGDP


1 0.002387 0.205478 1.236971 13.21736 2.655781 82.68441 0.000000

2 0.003040 0.217669 1.242535 10.28226 3.396029 84.66230 0.199203

3 0.003118 1.142413 1.187572 9.852772 4.130884 82.53336 1.152998

4 0.003360 3.773424 4.413958 8.570646 3.658512 77.35202 2.231443

5 0.003771 3.185940 4.289042 6.828458 3.005717 80.91504 1.775798

6 0.003929 3.816799 4.027672 6.334248 3.569495 80.38178 1.870004

7 0.004270 6.804054 3.868428 5.378906 3.049405 78.19182 2.707384

8 0.004343 8.663401 3.741106 5.481439 3.750771 75.74171 2.621568

9 0.004497 8.137240 3.549404 5.200924 4.157883 75.45835 3.496199

10 0.004630 7.686880 6.619383 5.536042 5.249254 71.60947 3.298975



to the change of GDP. Except its self, exchange

rate has biggest contribution to the change of

GDP and it contribution is around 8%. Another

variable that has big contribution to the change of

GDP is exchange rate.


165

Table 19. Variance Decomposition of LRR


1 0.007689 6.327794 3.549374 10.62128 3.932818 0.918582 74.65015

2 0.009562 7.659990 8.261595 14.54193 7.287504 3.672783 58.57620

3 0.012111 6.049033 12.31264 12.29283 25.31104 2.668204 41.36626

4 0.014167 5.276844 10.32942 18.28722 22.45448 2.937335 40.71471

5 0.016029 4.238347 9.880773 18.90839 20.59192 3.692840 42.68773

6 0.018763 3.144281 8.875669 15.60916 23.36482 4.270021 44.73605

7 0.022125 2.279741 7.293668 14.64063 25.59106 3.459859 46.73505

8 0.024856 1.811756 5.989161 15.46010 27.60795 2.978329 46.15271

9 0.027550 1.489315 4.879751 14.99328 29.10144 3.093112 46.44310

10 0.031114 1.792615 4.107954 14.97287 28.91558 2.721635 47.48935



to the change of reserve requirement. Except its

self, government expenditure has biggest

contribution to the change of reserve requirement

and it contribution is around 13%. Another

variable that has big contribution to the change of

reserve requirement is exchange rate and it

contribution is around 10%.

4. CONCLUSION

From the various explanations above, it can be

concluded that macroprudential policy,

monetary policy and fiscal policy have a long-

term relationship in maintaining economic

stability. In addition to relationships between

policies, the results also show that each variable

on economic stability has a different response to

changes in policy variables. Changes to a variable

will be responded to by other variables over a

certain period of time. This is because the

response from the public and the market to make

adjustments due to government policy

intervention requires a certain time span and the

government in responding to changes in

economic stability requires a certain timeframe

for formulating the policy. In addition,

coordination between policies is needed to

maintain economic stability because each

government policy responds differently by each

variable on economic stability.

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