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01042
Inflation and policy response: A case study of Indonesia during
the Covid-19 pandemic
Eka Briza Erdyana1, Ris Yuwono Yudo Nugrohoϭ*
1Faculty of Economics and Business, Trunojoyo Madura University, Indonesia
Abstract. Through the responses and contributions generated during the COVID-19 pandemic from March
2020 to April 2023, this research aims to analyse the monetary determinants of inflation in Indonesia. The
dependent variables in this research include consumer price index inflation (CPI) and core inflation. In
contrast, the independent variables are variables on the monetary side, which include interest rate policy,
money supply (M2), and the exchange rate of the United States dollar against the rupiah. This research uses
a quantitative approach with the Vector Error Correction Model (VECM) as the analysis method. According
to the research findings, CPI and core inflation responded negatively to shocks in policy interest rates. Both
CPI and core inflation responded positively to shocks or changes in the money supply. CPI and core inflation
respond positively to money supply shocks. CPI and core inflation respond negatively to exchange rate
shocks. Overall, core inflation responded better to the magnitude of changes in monetary side variables
during the COVID-19 pandemic than consumer price index (CPI) inflation. The interest rate policy variable
contributes more to consumer price index (CPI) inflation and core inflation than the money supply and
exchange rate variables.
1 Introduction
The government initially declared that the COVID-19
pandemic would reach Indonesia on March 2, 2020.
COVID-19 cases have shown an increase since it was
first announced, with 4,262,720 confirmed cases and
4,292 active cases until the end of 2021 (Ministry of
Health of the Republic of Indonesia, 2022). The increase
in COVID-19 cases has led the government to
implement a Large-Scale Social Restrictions (PSBB)
policy to overcome the health crisis quite effectively.
However, it affects economic performance. Inhibitions
in human mobility and activities for goods and services
reduce people's purchasing power and ultimately,
economic growth experiences a sharp decline, overall,
in 2020 amounting to -2.07 percent. The implementation
of PSBB by more than 31 regional governments has
hampered economic growth in several regions, causing
weak domestic demand and driving low inflation (Bank
Indonesia, 2021).
Inflation plays an important role in looking at the
stability of a country's economy. A growing economy
means that economic activity continues to experience
growth in various sectors, as long as inflation is within
the normal range (Silaban et al., 2021). Figure 1 shows
that the growth rates of the Consumer Price Index (CPI)
and Core Inflation are relatively the same, decreasing
and growing below Bank Indonesia's inflation target of
3 ± 1 percent in 2020–2021, which is also the lowest
inflation in the last ten years. Due to the government's
implementation of mobility policies as well as the
* Corresponding author: ris.nugroho@trunojoyo.ac.id
decline in global commodity prices during the COVID-
19 pandemic, domestic demand that year was unstable,
which had an impact on the low level of inflation (Bank
Indonesia, 2021).
Source: Bank Indonesia, 2023 (Data processed).
Fig.1. Inflation in Indonesia in 2013 – 2022.
The problem of inflation in Indonesia has been the
government's focus since 2000, it cannot be completely
eliminated but can only be controlled. According to
Özen et al., (2020), maintaining price stability by
ensuring that the inflation rate is controlled and as low
as possible is one of the main goals of a country's central
bank, because price stability indicators are believed to
be able to predict the future. Afonso et al., (2019)
revealed that inflation has a major impact on the
monetary authority's response in analyzing the
interaction of monetary and fiscal policy. Bank
Indonesia has the authority to determine and implement
monetary policy by taking into account the inflation rate
0,00
5,00
10,00
Percentage
Period (yearly)
IHK INTI
BIO Web of Conferences 146, 01042 (2024) https://doi.org/10.1051/bioconf/202414601042
BTMIC 2024
© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution
License 4.0 (https://creativecommons.org/licenses/by/4.0/).
target, as stated in Law no. 3 of 2004. The ultimate goal
of the policy focus is to maintain the stability of the
rupiah exchange rate.
The implementation of Bank Indonesia's monetary
policy develops based on economic developments and
the political climate of the Indonesian nation (Warijiyo
& Solikin, 2003). During the Covid - 19 pandemic, Bank
Indonesia coordinated with the government and the
Financial System Stability Committee (KSSK) to
maintain macroeconomic stability, the financial system,
and support National Economic Recovery (PEN). Bank
Indonesia funds and shares burdens in the 2020 APBN
through the purchase of Government Securities (SBN)
to implement Law no. 02 of 2020, through market
mechanisms regulated in the Joint Decree of the
Minister of Finance and the Governor of Bank Indonesia
dated 16 April 2020 amounting to 75.86 trillion rupiah
(Bank Indonesia, 2021).
Stock and Watson (2015) states that monetary policy
has more influence Core Inflation than Consumer Price
Index (CPI) Inflation. The statement is based on the fact
that Core Inflation reflects a more stable long-term
inflation trend, while CPI Inflation is more influenced
by temporary factors such as fluctuations in commodity
prices and changes in prices of certain goods.
Sertiartiti and Hapsari (2019) in their analysis stated
that inflation is determined by demand pull and cost
push, but the central bank is only able to control inflation
from the monetary aspect. According to Amaefula
(2016) interest rates and inflation are macroeconomic
indicators that are often linked to maintaining economic
balance. Makhrus and Priyadi (2022) reveal that interest
rates influence inflation positive and significant in the
short and long term. Bank Indonesia issued the BI – 7
Days Reverse Repo Rate (BI7DRR) as a new interest
rate policy which took effect on 19 August 2016 to
strengthen the effectiveness of monetary policy
transmission and function as a policy response to reduce
inflation according to target (Bank Indonesia, 2023).
During the Covid-19 pandemic in 2020, the policy
interest rate was reduced five times with a total
reduction of 125 bps to 3.75 percent and was reduced
again in 2021 by 25 bps to 3.50 percent (Bank Indonesia,
2021).
According to quantity theory, the amount of money
circulating in society determines the value of money and
the growth of the money supply determines the inflation
rate (Mankiw, 2021). In 2020 the amount of money in
circulation was 6,900,050 billion rupiah, an increase of
7,870,453 billion rupiah in 2021. The increase in the
amount of money in circulation in 2020 - 2021 when
associated with the reduction in Bank Indonesia's policy
interest rate in that year is something consistent, because
of this decrease, the increase in interest rates is expected
to respond to people's desire to take advantage of bank
loans, which will then trigger growth in the money
supply. Amankwah and Atta (2019) stated that growth in
the money supply has a short-term and long-term
relationship with the inflation rate. This assumption is in
line with the research results of Osman et al., (2019);
Esprance and Fuling (2020); and Cheti and Ilembo
(2021). In general, an increase in the money supply
which tends to be high is always accompanied by a high
level of inflation. However, the money supply (M2)
increased in 2020 to 2021, the inflation rate showed a
downward trend in that year in Figure 1.1.
In addition to policy interest rates and the money
supply, exchange rate is one of the most important prices
in an open economy has a very large influence on the
current account and other macroeconomic variables
(Amhimmid et al., 2021). Dornbusch (1976) states that
inflation and exchange rates have a vital importance in
developing countries. Exchange rate fluctuations
significantly affect the general price level and changes
in exchange rates will affect production costs, such as
the price of imported goods.
Fetai et al., (2016) explains that exchange rate
changes have a strong impact on inflation. Devia dan
Fadli (2022) states that the exchange rate has a negative
and significant effect on inflation in Indonesia for the
period 2004 – 2017. An increase in the Rupiah exchange
rate against the US Dollar makes the US Dollar currency
weaken or depreciate and reduce inflation. Monfared
and Akin (2017) analyzed the relationship between the
exchange rate and inflation in the Iranian case, revealing
that the exchange rate had a positive effect on inflation.
Several studies in analyzing the determinants of
inflation were carried out by reviewing the application
of monetary policy. Oktori (2019), the case of Nigeria
for the period 2009 - 2017 with the Error Correction
Model (ECM) model reveals that the money supply,
exchange rates, monetary policy interest rates, securities
and liquidity ratios are not only significant, but have an
influence on the inflation rate. Researchers argue that
the central bank needs to conduct periodic research to
determine the dynamics of changing relationships so
that policy interventions are far more effective and have
traction on the economy. Assa et al., (2020), explained
that the policy interest rate has a positive and significant
effect on the inflation rate, while the money supply
shows a negative and insignificant effect on inflation in
the case of Indonesia for the period 2006 – 2019.
Angelina and Nugraha (2020), examining inflation in
the Indonesian case using the Two Stage Least Squared
(TSLS) method state that inflation is positively and
significantly influenced by the money supply and
exchange rate, negatively and significantly influenced
by the SBI interest rate. Salih and Kabasakal (2021)
examine the case of Iraq inflation for the period 2014-
2020 using the Autoregressive Distributed Lagged
(ARDL) model, explaining that the money supply and
exchange rates have a positive and significant effect on
the inflation rate. An Increase in the money and
exchange rate high inflation, while the interest rate has
a contradictory and significant relationship with the
inflation rate. An increase in interest rates causes a
decrease in the inflation rate. Gabisa et al., (2022)
examined inflation in the Ethiopian case for the period
1980 – 2021 using Auto – Regressive Distributed Lag
(ARDL). The results reveal that inflation expectations,
real Gross Domestic Product (GDP), money supply and
real interest rates are the main determinants of inflation
both in the short and long term.
Inflation is a major problem affecting various
aspects of macro-level economic activity, which is
important for a controlled and planned rate of economic
growth. Research on the factors that influence the
inflation rate needs to be carried out, so that the
empirical results obtained can be used as views by
policy makers. Based on the description of the
background above and the importance of the problem of
inflation, the authors are interested in conducting
research entitled "Determinants of Inflation in Indonesia
From The Monetary Side During the Covid-19
Pandemic". This study makes a significant contribution
to the understanding of the factors influencing inflation
in Indonesia, particularly in the context of the COVID-
19 pandemic.
2 Research method
This study uses a quantitative approach method.
Creswell (2018) explains that a quantitative approach is
used to test objective theories. The quantitative
approach aims to prove, upload or give credence to
existing theories. This approach involves measuring
variables and examining the relationships between
variables to uncover patterns, correlations or causal
relationships, using a linear data collection and analysis
method that produces statistical data (Leavy, 2023).
Research data analysis techniques use the Vector Error
Correction Model (VECM) method. The basic model
used, as follows:
(1)
(2)
is Consumer Price Index (CPI) Inflation in the
first model, is Core Inflation in the second model.
is the policy interest rate, is the money supply in
a broad sense (M2) and is the exchange rate of the
US Dollar against the Indonesian Rupiah as a variable
on the monetary side of both models. is a constant and
is error term.
Stationarity test of research data or unit root test is
an initial stage that must be carried out before estimating
VECM, aims to determine whether the variable data
used is stationary or non-stationary. The stationarity test
was developed by Dickey-Fuller and is known as the
Augmented Dickey-Fuller (ADF) test through the
following equation (Gujarati & Porter, 2008):
(3)
Information:
= pure white noise error
etc.
Optimal lag testing is needed in VECM modeling,
aims to ensure that the estimated model is able to
interpret dynamically, efficiency and comprehensive.
Lutkepohl (2005) explained that the optimal lag test was
seen based on the recommended information, through
the following approach:
(4)
(5)
(6)
(7)
Stability test in the estimation of Vector Error
Correction Model (VECM) is performed for analysis of
Impulse Response Function (IRF) and Variance
Decomposition (VD) is valid to perform. Knowing the
stability level of the model by looking at the
characteristics of the Polynomial Inverse Root through
the modulus value in the table. A model is said to be
stable if all of its roots or roots have a modulus smaller
than one (Damayanti & Jalunggono, 2022).
The cointegration test identifies whether the model
in the study indicates a long-term relationship between
variables, and integrates with each other in the same
order (Gujarati & Porter, 2008). The cointegration test
in the study was carried out using the Johansen
approach, based on the two tests suggested by Johansen
(1988) and Osterwald-Lenum (1992), namely the
statistical trace test and the maximum eigenvalue, which
is written in the following equation (Bedada et al.,
2020):
(8)
(9)
Where
is the sum of the estimated
eigenvalues and “r” implements the cointegration
relationship. Detecting the existence of cointegration in
the model is carried out with the following hypothesis
conditions:
: no cointegration
: there is cointegration
Criteria for decision making, as follows:
• The results of the trace statistic value and eigenvalue
> 0,05, then s rejected, meaning that there is
cointegration between the variables in the research
model, then the estimation of the Vector Error
Correction Model (VECM) can be done
(Laksahmanasany, 2022).
• The results of the trace statistical value of the
eigenvalue < 0,05, then is accepted, meaning that
there is no cointegration between the variables in the
research model, so the Vector Autoregression
(VAR) method can be used (Laksahmanasany,
2022).
Vector Error Correction Model (VECM) has
advantages in explaining short-term and long-term
economic phenomena, as well as being a solution to the
problem of non-stationary time series variables at the
2
BIO Web of Conferences 146, 01042 (2024) https://doi.org/10.1051/bioconf/202414601042
BTMIC 2024
target, as stated in Law no. 3 of 2004. The ultimate goal
of the policy focus is to maintain the stability of the
rupiah exchange rate.
The implementation of Bank Indonesia's monetary
policy develops based on economic developments and
the political climate of the Indonesian nation (Warijiyo
& Solikin, 2003). During the Covid - 19 pandemic, Bank
Indonesia coordinated with the government and the
Financial System Stability Committee (KSSK) to
maintain macroeconomic stability, the financial system,
and support National Economic Recovery (PEN). Bank
Indonesia funds and shares burdens in the 2020 APBN
through the purchase of Government Securities (SBN)
to implement Law no. 02 of 2020, through market
mechanisms regulated in the Joint Decree of the
Minister of Finance and the Governor of Bank Indonesia
dated 16 April 2020 amounting to 75.86 trillion rupiah
(Bank Indonesia, 2021).
Stock and Watson (2015) states that monetary policy
has more influence Core Inflation than Consumer Price
Index (CPI) Inflation. The statement is based on the fact
that Core Inflation reflects a more stable long-term
inflation trend, while CPI Inflation is more influenced
by temporary factors such as fluctuations in commodity
prices and changes in prices of certain goods.
Sertiartiti and Hapsari (2019) in their analysis stated
that inflation is determined by demand pull and cost
push, but the central bank is only able to control inflation
from the monetary aspect. According to Amaefula
(2016) interest rates and inflation are macroeconomic
indicators that are often linked to maintaining economic
balance. Makhrus and Priyadi (2022) reveal that interest
rates influence inflation positive and significant in the
short and long term. Bank Indonesia issued the BI – 7
Days Reverse Repo Rate (BI7DRR) as a new interest
rate policy which took effect on 19 August 2016 to
strengthen the effectiveness of monetary policy
transmission and function as a policy response to reduce
inflation according to target (Bank Indonesia, 2023).
During the Covid-19 pandemic in 2020, the policy
interest rate was reduced five times with a total
reduction of 125 bps to 3.75 percent and was reduced
again in 2021 by 25 bps to 3.50 percent (Bank Indonesia,
2021).
According to quantity theory, the amount of money
circulating in society determines the value of money and
the growth of the money supply determines the inflation
rate (Mankiw, 2021). In 2020 the amount of money in
circulation was 6,900,050 billion rupiah, an increase of
7,870,453 billion rupiah in 2021. The increase in the
amount of money in circulation in 2020 - 2021 when
associated with the reduction in Bank Indonesia's policy
interest rate in that year is something consistent, because
of this decrease, the increase in interest rates is expected
to respond to people's desire to take advantage of bank
loans, which will then trigger growth in the money
supply. Amankwah and Atta (2019) stated that growth in
the money supply has a short-term and long-term
relationship with the inflation rate. This assumption is in
line with the research results of Osman et al., (2019);
Esprance and Fuling (2020); and Cheti and Ilembo
(2021). In general, an increase in the money supply
which tends to be high is always accompanied by a high
level of inflation. However, the money supply (M2)
increased in 2020 to 2021, the inflation rate showed a
downward trend in that year in Figure 1.1.
In addition to policy interest rates and the money
supply, exchange rate is one of the most important prices
in an open economy has a very large influence on the
current account and other macroeconomic variables
(Amhimmid et al., 2021). Dornbusch (1976) states that
inflation and exchange rates have a vital importance in
developing countries. Exchange rate fluctuations
significantly affect the general price level and changes
in exchange rates will affect production costs, such as
the price of imported goods.
Fetai et al., (2016) explains that exchange rate
changes have a strong impact on inflation. Devia dan
Fadli (2022) states that the exchange rate has a negative
and significant effect on inflation in Indonesia for the
period 2004 – 2017. An increase in the Rupiah exchange
rate against the US Dollar makes the US Dollar currency
weaken or depreciate and reduce inflation. Monfared
and Akin (2017) analyzed the relationship between the
exchange rate and inflation in the Iranian case, revealing
that the exchange rate had a positive effect on inflation.
Several studies in analyzing the determinants of
inflation were carried out by reviewing the application
of monetary policy. Oktori (2019), the case of Nigeria
for the period 2009 - 2017 with the Error Correction
Model (ECM) model reveals that the money supply,
exchange rates, monetary policy interest rates, securities
and liquidity ratios are not only significant, but have an
influence on the inflation rate. Researchers argue that
the central bank needs to conduct periodic research to
determine the dynamics of changing relationships so
that policy interventions are far more effective and have
traction on the economy. Assa et al., (2020), explained
that the policy interest rate has a positive and significant
effect on the inflation rate, while the money supply
shows a negative and insignificant effect on inflation in
the case of Indonesia for the period 2006 – 2019.
Angelina and Nugraha (2020), examining inflation in
the Indonesian case using the Two Stage Least Squared
(TSLS) method state that inflation is positively and
significantly influenced by the money supply and
exchange rate, negatively and significantly influenced
by the SBI interest rate. Salih and Kabasakal (2021)
examine the case of Iraq inflation for the period 2014-
2020 using the Autoregressive Distributed Lagged
(ARDL) model, explaining that the money supply and
exchange rates have a positive and significant effect on
the inflation rate. An Increase in the money and
exchange rate high inflation, while the interest rate has
a contradictory and significant relationship with the
inflation rate. An increase in interest rates causes a
decrease in the inflation rate. Gabisa et al., (2022)
examined inflation in the Ethiopian case for the period
1980 – 2021 using Auto – Regressive Distributed Lag
(ARDL). The results reveal that inflation expectations,
real Gross Domestic Product (GDP), money supply and
real interest rates are the main determinants of inflation
both in the short and long term.
Inflation is a major problem affecting various
aspects of macro-level economic activity, which is
important for a controlled and planned rate of economic
growth. Research on the factors that influence the
inflation rate needs to be carried out, so that the
empirical results obtained can be used as views by
policy makers. Based on the description of the
background above and the importance of the problem of
inflation, the authors are interested in conducting
research entitled "Determinants of Inflation in Indonesia
From The Monetary Side During the Covid-19
Pandemic". This study makes a significant contribution
to the understanding of the factors influencing inflation
in Indonesia, particularly in the context of the COVID-
19 pandemic.
2 Research method
This study uses a quantitative approach method.
Creswell (2018) explains that a quantitative approach is
used to test objective theories. The quantitative
approach aims to prove, upload or give credence to
existing theories. This approach involves measuring
variables and examining the relationships between
variables to uncover patterns, correlations or causal
relationships, using a linear data collection and analysis
method that produces statistical data (Leavy, 2023).
Research data analysis techniques use the Vector Error
Correction Model (VECM) method. The basic model
used, as follows:
(1)
(2)
is Consumer Price Index (CPI) Inflation in the
first model, is Core Inflation in the second model.
is the policy interest rate, is the money supply in
a broad sense (M2) and is the exchange rate of the
US Dollar against the Indonesian Rupiah as a variable
on the monetary side of both models. is a constant and
is error term.
Stationarity test of research data or unit root test is
an initial stage that must be carried out before estimating
VECM, aims to determine whether the variable data
used is stationary or non-stationary. The stationarity test
was developed by Dickey-Fuller and is known as the
Augmented Dickey-Fuller (ADF) test through the
following equation (Gujarati & Porter, 2008):
(3)
Information:
= pure white noise error
etc.
Optimal lag testing is needed in VECM modeling,
aims to ensure that the estimated model is able to
interpret dynamically, efficiency and comprehensive.
Lutkepohl (2005) explained that the optimal lag test was
seen based on the recommended information, through
the following approach:
(4)
(5)
(6)
(7)
Stability test in the estimation of Vector Error
Correction Model (VECM) is performed for analysis of
Impulse Response Function (IRF) and Variance
Decomposition (VD) is valid to perform. Knowing the
stability level of the model by looking at the
characteristics of the Polynomial Inverse Root through
the modulus value in the table. A model is said to be
stable if all of its roots or roots have a modulus smaller
than one (Damayanti & Jalunggono, 2022).
The cointegration test identifies whether the model
in the study indicates a long-term relationship between
variables, and integrates with each other in the same
order (Gujarati & Porter, 2008). The cointegration test
in the study was carried out using the Johansen
approach, based on the two tests suggested by Johansen
(1988) and Osterwald-Lenum (1992), namely the
statistical trace test and the maximum eigenvalue, which
is written in the following equation (Bedada et al.,
2020):
(8)
(9)
Where
is the sum of the estimated
eigenvalues and “r” implements the cointegration
relationship. Detecting the existence of cointegration in
the model is carried out with the following hypothesis
conditions:
: no cointegration
: there is cointegration
Criteria for decision making, as follows:
• The results of the trace statistic value and eigenvalue
> 0,05, then s rejected, meaning that there is
cointegration between the variables in the research
model, then the estimation of the Vector Error
Correction Model (VECM) can be done
(Laksahmanasany, 2022).
• The results of the trace statistical value of the
eigenvalue < 0,05, then is accepted, meaning that
there is no cointegration between the variables in the
research model, so the Vector Autoregression
(VAR) method can be used (Laksahmanasany,
2022).
Vector Error Correction Model (VECM) has
advantages in explaining short-term and long-term
economic phenomena, as well as being a solution to the
problem of non-stationary time series variables at the
3
BIO Web of Conferences 146, 01042 (2024) https://doi.org/10.1051/bioconf/202414601042
BTMIC 2024
level. The assumptions that must be met in the VECM
analysis are that all variables in the study must be
stationary in the first derivative and cointegrated.
The Vector Error Correction Model (VECM) is used
due to its ability to capture both short-term dynamics
and long-term relationships among the variables. This is
marked with an average value of zero, constant variance
and between the dependent variables there is no
correlation. In general, the Vector Error Correction
Model (VECM) model is as follows:
(10)
Information:
= The vector that contains the variables
analyzed in the study
µ0x = Interception vector
µ1x = Regression coefficient vector
t = Time trendd
= , where b contains the long-run
cointegration equation
= In-level variables
= Regression coefficient matrix
= The VECM and VAR orders
ɛt = error term
The specifications of the VECM equation model
used in the research are as follows:
(11)
(12)
Information:
= Consumer Price Index (CPI) Inflation
= Core Inflation
= Interest Rate Policy
= Money Supply
= Exchange Rate
= constant
= long run coefficient
= Speed of adjustment parameter with a
negative sign
= error corection term
= error term
Impulse Response Function (IRF) describes the
rate of shock of one variable to another over a certain
period of time, so that the duration of the shock effect of
a variable on other variables is obtained until the effect
returns to the balance point (Enders, 2015). IRF analysis
tracks the reactions of endogenous variables in the
VECM system to shocks or one-off changes to any of
the current and future value innovations. IRF can be
determined through the equation (Laksahmanasany,
2022), as follows:
(13)
s the endogenous dependent variable vector, s the
endogenous dependent variable vector, is the
innovation vector and is the parameter vector that
measures the dependent variable's reaction to
innovation in all variables including those included in
the VECM model.
Variance decomposition or error variant
decomposition reveals the relationship between
variables in the system by providing an estimate of the
proportion of movement in the sequence due to shocks
from one variable to another (Enders, 2015).
3 Results and discussion
3.1 Results
In this study, there are two models analyzed, the first
model uses Consumer Price Index (CPI) Inflation and
the second model uses Core Inflation as the dependent
variable. The independent variables in both models
include the policy interest rate, the money supply (M2)
and the exchange rate of the United States Dollar against
the Indonesian Rupiah. Both models were analyzed
using the Vector Error Correction Model (VECM)
method, which was carried out with the help of the
Eviews-12 computer program, to obtain the best
research results.
Descriptive statistics show the true nature of the
research data, based on table 1 CPI inflation has a mean
value of 2.84 with a standard deviation of 1.585351421.
The Core Inflation variable has a mean value of 2.20
with a standard deviation of 0.719222996. The policy
interest rate variable has a mean value of 4.07 with a
standard deviation of 0.76236347. The money supply
variable has a mean value of 7,395,945.19 with a
standard deviation of 654669.58. The exchange rate
variable has a mean of 14,706 with a standard deviation
of 509.8504703.
Table 1. Variable descriptive statistics.
Variable
N
Max
Min
Mean
Standard
Deviation
CPI Inflation (%)
38
5,95
1,32
2,84
1,585351421
Core Inflation (%)
38
3,36
1,18
2,20
0,719222996
Policy Interest Rate
(%)
38
5,75
3,50
4,07
0,76236347
Money Supply M2
(Billion Rupiah)
38
8.528.022,31
6.238.267,00
7,395,945.19
654669,58
USD/IDR Exchange
Rate (Thousand
rupiah)
38
16.367
14.084
14.706
509,8504703
Source: processed data, 2023
Table 2 is the result of stationarity test of research
variable data. At the level level it can be seen that all the
variables in the study are not stationary, the ADF
probability values produced by all variables are greater
than the significance level of 1 percent, 5 percent and 10
percent, so it is necessary to test the stationarity of the
data on the first derivative or 1st difference.
Table 2. Data stationarity test results.
The results of the research data stationarity test in the
first difference in table 2, it is known that overall, the
variables are stationary at the 1st difference level,
having a probability value of ADF smaller than the
significance level of 1 percent and 5 percent. After the
overall data is stationary at the same level, then
determine the optimal lag length.
Based on table 3 the results of examining the optimal
lag length in the first model, it can be seen that the
criterion information that has the smallest value is
indicated by the LR, FPE, SC and HQ criteria with the
most asterisks being in lag 1, so it is concluded that the
optimal lag length is used in the first model is at lag 1.
Table 3. Optimal leg test result.
The results of examining the optimal lag length in
the second model, it can be seen that the criterion
information that has the smallest value is shown by the
LR, PFE, AIC, SC and HQ criteria with the most
asterisks being in lag 1, so it is concluded that the
optimal lag length used in the second model is at lag 1.
Based on table 4 the results of stability testing in the
first model and the second model are stable, this can be
seen from the modulus range with an average value
obtained in each model less than one, thus the results of
the Impulse Response Function (IRF) and Variance
analysis Decomposition (VD) of each model is valid.
Next, do the Johansen cointegration test on each model.
Table 4. VAR stability test result.
Based on the results of the cointegration test of the
first model in table 5, it is known that all trace statistical
values obtained are greater than the critical value of 5
percent, so reject H0. There are 4 cointegration or long-
term relationships detected between the Consumer Price
Index (CPI) Inflation variables, policy interest rates,
money supply and exchange rates. This means that the
first model can be continued using the Vector Error
Correction Model (VECM) method.
Table 5. Johansen cointegration test result.
The results of the second model cointegration test in
table 3.5, it is known that the three trace statistics values
at rank 0, 1 and 2 are greater than the critical value of 5
percent, so reject H0. There are 3 cointegration or long-
term relationships detected between Core Inflation,
policy interest rates, money supply and exchange rates.
This means that the second model can be continued
using the Vector Error Correction Model (VECM)
method. The following is the result of representing the
VECM estimation of the two models:
Model I
D(IHK)
= 0.618179800759∗ (IHK(−1)
−6.96477439584∗LN_M2(−1)
−61.979864499∗LN_KURS(−1)
+701.563501963) −0.555467222131
∗(BI7DRR(−1) −3.70115549907
∗LN_M2(−1)− 19.0477535821
∗LN_KURS(−1) +236.96759351)
−0.421476319427∗D(IHK(−1))
−0.107960798849
∗D(BI7DRR(−1))0.441341879441
∗D(LN_M2(−1)) −0.856504244375
∗D(LN_KURS(−1))
(13)
Model II
Variabel
Tingkat Level I(0)
Tingkat 1st Difference I(1)
T-statistic
Prob.
T-statistic
Prob.
IHK
-2.065294
0.5458
-5.390998
0.0017***
INTI
-2.929980
0.1655
-2.636919
0.0098***
BI7DRR
-1.770459
0.6980
-3.911335
0.0236**
LN_M2
-3.194886
0.1019
-9.197334
0.0000***
LN_KURS
-0.802812
0.3614
-6.040681
0.0000***
Note: *** and ** indicate significant at the 1% and 5% level
Source: Results of Output Eviews-12, 2023 (Data processed)
Table 3.3 Optimal Lag Test Results
Model I
Lag
LogL
LR
FPE
AIC
SC
HQ
0
59.53265
NA
4.92e-07
-3.173294
-2.995540
-3.111934
1
225.6740
284.8137*
9.32e-11*
-11.75280
-10.86403*
-11.44600*
2
242.0707
24.36082
9.48e-11
-11.77547*
-10.17568
-11.22322
3
252.4567
13.05674
1.45e-10
-11.45467
-9.143867
-10.65698
Model II
Lag
LogL
LR
FPE
AIC
SC
HQ
0
78.73897
NA
1.64e-07
-4.270798
-4.093044
-4.209438
1
264.4068
318.2877*
1.02e-11*
-13.96610*
-13.07733*
-13.65930*
2
276.6662
18.21395
1.31e-11
-13.75235
-12.15257
-13.20011
3
287.9672
14.20703
1.90e-11
-13.48384
-11.17304
-12.68615
Source: Results of Output Eviews-12, 2023 (Data processed)
Table 3.4 VAR Stability Test Results
Model I
Model II
Root
Modulus
Root
Modulus
0.954055
0.954055
0.981625
0.981625
0.931038 – 0.177202i
0.947752
0.942466 - 0.148337i
0.954069
0.931038 + 0.177202i
0.947752
0.942466 + 0.148337i
0.954069
0.301785
0.301785
0.377930
0.377930
Source: Results of Output Eviews-12, 2023 (Data processed)
Table 3.5 Johansen Cointegration Test Results
Rank
Model I
Rank
Model II
Trace
Statistic
5% Critical
Value
Prob.
Trace
Statistic
5% Critical
Value
Prob.
0*
86.95745
54.07904
0.0000
0*
74.77953
54.07904
0.0003
1*
56.38409
35.19275
0.0001
1*
40.95405
35.19275
0.0107
2*
31.10267
20.26184
0.0011
2*
20.35655
20.26184
0.0485
3*
9.926685
9.164546
0.0358
3
5.885157
9.164546
0.1998
Source: Results of Output Eviews-12, 2023 (Data processed)
4
BIO Web of Conferences 146, 01042 (2024) https://doi.org/10.1051/bioconf/202414601042
BTMIC 2024
level. The assumptions that must be met in the VECM
analysis are that all variables in the study must be
stationary in the first derivative and cointegrated.
The Vector Error Correction Model (VECM) is used
due to its ability to capture both short-term dynamics
and long-term relationships among the variables. This is
marked with an average value of zero, constant variance
and between the dependent variables there is no
correlation. In general, the Vector Error Correction
Model (VECM) model is as follows:
(10)
Information:
= The vector that contains the variables
analyzed in the study
µ0x = Interception vector
µ1x = Regression coefficient vector
t = Time trendd
= , where b contains the long-run
cointegration equation
= In-level variables
= Regression coefficient matrix
= The VECM and VAR orders
ɛt = error term
The specifications of the VECM equation model
used in the research are as follows:
(11)
(12)
Information:
= Consumer Price Index (CPI) Inflation
= Core Inflation
= Interest Rate Policy
= Money Supply
= Exchange Rate
= constant
= long run coefficient
= Speed of adjustment parameter with a
negative sign
= error corection term
= error term
Impulse Response Function (IRF) describes the
rate of shock of one variable to another over a certain
period of time, so that the duration of the shock effect of
a variable on other variables is obtained until the effect
returns to the balance point (Enders, 2015). IRF analysis
tracks the reactions of endogenous variables in the
VECM system to shocks or one-off changes to any of
the current and future value innovations. IRF can be
determined through the equation (Laksahmanasany,
2022), as follows:
(13)
s the endogenous dependent variable vector, s the
endogenous dependent variable vector, is the
innovation vector and is the parameter vector that
measures the dependent variable's reaction to
innovation in all variables including those included in
the VECM model.
Variance decomposition or error variant
decomposition reveals the relationship between
variables in the system by providing an estimate of the
proportion of movement in the sequence due to shocks
from one variable to another (Enders, 2015).
3 Results and discussion
3.1 Results
In this study, there are two models analyzed, the first
model uses Consumer Price Index (CPI) Inflation and
the second model uses Core Inflation as the dependent
variable. The independent variables in both models
include the policy interest rate, the money supply (M2)
and the exchange rate of the United States Dollar against
the Indonesian Rupiah. Both models were analyzed
using the Vector Error Correction Model (VECM)
method, which was carried out with the help of the
Eviews-12 computer program, to obtain the best
research results.
Descriptive statistics show the true nature of the
research data, based on table 1 CPI inflation has a mean
value of 2.84 with a standard deviation of 1.585351421.
The Core Inflation variable has a mean value of 2.20
with a standard deviation of 0.719222996. The policy
interest rate variable has a mean value of 4.07 with a
standard deviation of 0.76236347. The money supply
variable has a mean value of 7,395,945.19 with a
standard deviation of 654669.58. The exchange rate
variable has a mean of 14,706 with a standard deviation
of 509.8504703.
Table 1. Variable descriptive statistics.
Variable
N
Max
Min
Mean
Standard
Deviation
CPI Inflation (%)
38
5,95
1,32
2,84
1,585351421
Core Inflation (%)
38
3,36
1,18
2,20
0,719222996
Policy Interest Rate
(%)
38
5,75
3,50
4,07
0,76236347
Money Supply M2
(Billion Rupiah)
38
8.528.022,31
6.238.267,00
7,395,945.19
654669,58
USD/IDR Exchange
Rate (Thousand
rupiah)
38
16.367
14.084
14.706
509,8504703
Source: processed data, 2023
Table 2 is the result of stationarity test of research
variable data. At the level level it can be seen that all the
variables in the study are not stationary, the ADF
probability values produced by all variables are greater
than the significance level of 1 percent, 5 percent and 10
percent, so it is necessary to test the stationarity of the
data on the first derivative or 1st difference.
Table 2. Data stationarity test results.
The results of the research data stationarity test in the
first difference in table 2, it is known that overall, the
variables are stationary at the 1st difference level,
having a probability value of ADF smaller than the
significance level of 1 percent and 5 percent. After the
overall data is stationary at the same level, then
determine the optimal lag length.
Based on table 3 the results of examining the optimal
lag length in the first model, it can be seen that the
criterion information that has the smallest value is
indicated by the LR, FPE, SC and HQ criteria with the
most asterisks being in lag 1, so it is concluded that the
optimal lag length is used in the first model is at lag 1.
Table 3. Optimal leg test result.
The results of examining the optimal lag length in
the second model, it can be seen that the criterion
information that has the smallest value is shown by the
LR, PFE, AIC, SC and HQ criteria with the most
asterisks being in lag 1, so it is concluded that the
optimal lag length used in the second model is at lag 1.
Based on table 4 the results of stability testing in the
first model and the second model are stable, this can be
seen from the modulus range with an average value
obtained in each model less than one, thus the results of
the Impulse Response Function (IRF) and Variance
analysis Decomposition (VD) of each model is valid.
Next, do the Johansen cointegration test on each model.
Table 4. VAR stability test result.
Based on the results of the cointegration test of the
first model in table 5, it is known that all trace statistical
values obtained are greater than the critical value of 5
percent, so reject H0. There are 4 cointegration or long-
term relationships detected between the Consumer Price
Index (CPI) Inflation variables, policy interest rates,
money supply and exchange rates. This means that the
first model can be continued using the Vector Error
Correction Model (VECM) method.
Table 5. Johansen cointegration test result.
The results of the second model cointegration test in
table 3.5, it is known that the three trace statistics values
at rank 0, 1 and 2 are greater than the critical value of 5
percent, so reject H0. There are 3 cointegration or long-
term relationships detected between Core Inflation,
policy interest rates, money supply and exchange rates.
This means that the second model can be continued
using the Vector Error Correction Model (VECM)
method. The following is the result of representing the
VECM estimation of the two models:
Model I
D(IHK)
= 0.618179800759∗ (IHK(−1)
−6.96477439584∗LN_M2(−1)
−61.979864499∗LN_KURS(−1)
+701.563501963) −0.555467222131
∗(BI7DRR(−1) −3.70115549907
∗LN_M2(−1)− 19.0477535821
∗LN_KURS(−1) +236.96759351)
−0.421476319427∗D(IHK(−1))
−0.107960798849
∗D(BI7DRR(−1))0.441341879441
∗D(LN_M2(−1)) −0.856504244375
∗D(LN_KURS(−1))
(13)
Model II
Variabel
Tingkat Level I(0)
Tingkat 1st Difference I(1)
T-statistic
Prob.
T-statistic
Prob.
IHK
-2.065294
0.5458
-5.390998
0.0017***
INTI
-2.929980
0.1655
-2.636919
0.0098***
BI7DRR
-1.770459
0.6980
-3.911335
0.0236**
LN_M2
-3.194886
0.1019
-9.197334
0.0000***
LN_KURS
-0.802812
0.3614
-6.040681
0.0000***
Note: *** and ** indicate significant at the 1% and 5% level
Source: Results of Output Eviews-12, 2023 (Data processed)
Table 3.3 Optimal Lag Test Results
Model I
Lag
LogL
LR
FPE
AIC
SC
HQ
0
59.53265
NA
4.92e-07
-3.173294
-2.995540
-3.111934
1
225.6740
284.8137*
9.32e-11*
-11.75280
-10.86403*
-11.44600*
2
242.0707
24.36082
9.48e-11
-11.77547*
-10.17568
-11.22322
3
252.4567
13.05674
1.45e-10
-11.45467
-9.143867
-10.65698
Model II
Lag
LogL
LR
FPE
AIC
SC
HQ
0
78.73897
NA
1.64e-07
-4.270798
-4.093044
-4.209438
1
264.4068
318.2877*
1.02e-11*
-13.96610*
-13.07733*
-13.65930*
2
276.6662
18.21395
1.31e-11
-13.75235
-12.15257
-13.20011
3
287.9672
14.20703
1.90e-11
-13.48384
-11.17304
-12.68615
Source: Results of Output Eviews-12, 2023 (Data processed)
Table 3.4 VAR Stability Test Results
Model I
Model II
Root
Modulus
Root
Modulus
0.954055
0.954055
0.981625
0.981625
0.931038 – 0.177202i
0.947752
0.942466 - 0.148337i
0.954069
0.931038 + 0.177202i
0.947752
0.942466 + 0.148337i
0.954069
0.301785
0.301785
0.377930
0.377930
Source: Results of Output Eviews-12, 2023 (Data processed)
Table 3.5 Johansen Cointegration Test Results
Rank
Model I
Rank
Model II
Trace
Statistic
5% Critical
Value
Prob.
Trace
Statistic
5% Critical
Value
Prob.
0*
86.95745
54.07904
0.0000
0*
74.77953
54.07904
0.0003
1*
56.38409
35.19275
0.0001
1*
40.95405
35.19275
0.0107
2*
31.10267
20.26184
0.0011
2*
20.35655
20.26184
0.0485
3*
9.926685
9.164546
0.0358
3
5.885157
9.164546
0.1998
Source: Results of Output Eviews-12, 2023 (Data processed)
(14)
5
BIO Web of Conferences 146, 01042 (2024) https://doi.org/10.1051/bioconf/202414601042
BTMIC 2024
D(INTI) = 0.03259008037617∗ (INTI(−1)
−0.858299127253
∗LN_M2(−1)
−51.6868366824
∗LN_KURS(−1)
+507.085036362)
−0.0402467051129
∗(BI7DRR(−1)
−11.5383597313
∗LN_M2(−1)
−11.9090260007
∗LN_KURS(−1)
+293.320690786)
+0.44374993191
∗D(INTI(−1))
+0.290354326274
∗D(BI7DRR(1))
+0.525091134463
∗D(LN_M2(−1))
+0.967400736339
∗D(LN_KURS(−1))
(14)
3.2 Discussion
The results of the IRF analysis in this study are
presented in graphical form showing positive or
negative responses over 38 periods, according to the
time span of observations during the Covid - 19
Pandemic. The horizontal axis describes the time in the
next day after a shock occurs, while the vertical axis
describes the response value. The response generated in
the short term is usually quite significant and tends to
change, while in the long term it is consistent and tends
to shrink.
3.2.1 Interest rate
Figures 3.1 and 3.2 show that the variable policy interest
rates on inflation Consumer Price Index (CPI) and Core
Inflation show the same response. CPI inflation and core
inflation tend to respond negative to shocks or changes
in the policy interest rate variable. CPI inflation
responded negative from the third period to the last
period with the magnitude of the shock given -23.0
percent, while Core Inflation responded negative from
the sixth period to the last period with a shock amount
of -5.5 percent. The resulting negative response
explained that the movement of the policy interest rate
with CPI inflation and core inflation was not in the same
direction, where an increase in the policy interest rate
will reduce the CPI inflation rate and core inflation,
conversely, a decrease in the policy interest rate will
increase the CPI Inflation and Core Inflation rates. The
results of the IRF analysis are supported by the results
of research by Lelo et al., (2018); Esprance and Fuling
(2020); Junaeldi and Sentosa (2022); Ratri and
Munawar (2022).
The contraction in economic growth in the second
quarter of 2020 was -5.32 percent until the first quarter
of 2021 was -0.69 percent due to the Covid-19
pandemic, encouraging Bank Indonesia to implement
expansionary monetary policy in support of national
economic recovery, one of which is lowering policy
interest rates. In 2020 Bank Indonesia reduced the
policy interest rate five times with a total reduction of
125 bps to 3.75 percent at the end of the year, in 2021 it
will reduce the policy rate again by 25 bps to 3.50
percent. Changes in short-term interest rates are
transmitted to long-term interest rates in the supply and
demand side of the money market, which then affect the
cost of investment capital. A decrease in interest rates
makes the cost of new physical capital cheaper and
investments made profitable, resulting in an increase in
aggregate demand at a certain price level, thus shifting
the AD curve to the right (Mishkin, 2019). Lower
interest rates throughout 2020 and 2021 trigger an
increase in CPI inflation in early 2022 until it grows by
5.95 percent in September 2022.
Stock and Watson (2015) explain that policy interest
rates affect Core Inflation through the monetary policy
transmission mechanism, when the policy interest rate is
raised it will reduce aggregate demand in the economy,
conversely when the policy interest rate is lowered it
will encourage economic growth and increase the
inflation rate. The policy interest rate does not always
affect Core Inflation directly, but varies depending on
economic conditions and monetary policy. The
application of monetary policy influences the core
money supply, through open market operations. Core
money consists of currency circulating in the
community and balances in demand deposits owned by
commercial banks at Bank Indonesia. The central bank
raises interest rates to attract money in the community,
this will reduce the money supply and reduce inflation,
and vice versa, low interest rates will increase the money
supply and increase inflation (Tjahjono et al., 2000).
During the Covid – 19 Pandemic, the low monetary
policy interest rate continued to drive a reduction in
Model I
Model II
Source: Eviews-12, 2023 (processed)
Figure 3.1
Results of CPI Inflation IRF Analysis on
Policy Interest Rates
Source: Eviews-12, 2023 (processed)
Figure 3.2
Results of Core Inflation IRF Analysis on
Policy Interest Rates
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Source: Indonesian Economic and Financial Statistics (SEKI), 2023. (Data process ed)
Figure 4.7 Core Money Growth During the Covid-19 Pandemic
bank lending rates as an effort to facilitate and increase
financing (credit) for the business world. Asmadina et
al., (2021) revealed that lending had a positive and
significant effect on inflation in Indonesia during the
Covid - 19 Pandemic. The reduction in bank lending
rates has stimulated an increase in core money growth
as shown in Figure 4.6, in March 2020 core money
amounted to 1,031,285.12 billion rupiahs or grew by
3.56 percent to 1,544,963.73 billion rupiahs or grew by
28. 13 percent at the beginning of 2023. This core
money growth triggered an increase in inflation in early
2022 until January 2023 growing by 3.27 percent.
3.2.2 Monney supply
Figures 4.3 and 4.4 show that the money supply variable
(M2) has the same response to Consumer Price Index
(CPI) Inflation and Core Inflation. CPI inflation and
core inflation tend to respond positive to shocks or
changes in the variable money supply (M2) from the
beginning of the period to the last period. The magnitude
of the shock given by the money supply variable was
CPI Inflation of 0.8 percent and Core Inflation of 1.89
percent. The resulting positive response explains that the
movement of the money supply (M2) with CPI Inflation
and Core Inflation is one way, where an increase in the
money supply (M2) will increase the level of CPI
Inflation and Core Inflation, conversely, a decrease in
the money supply (M2) will reduce the level of CPI
inflation and core inflation. The results of the IRF
analysis are supported by the research results of Bedada
et al., (2019); Nigguse et al., (2019); Atil and Saouli
(2020); Damayanti and Jalunggono (2022);
Laksamanany (2022).
Based on empirical results, the relationship between
the money supply (M2) and inflation is expected to be
positive and unidirectional, but in fact the increase in the
money supply (M2) during the Covid-19 Pandemic was
not followed by high inflation rates, both Consumer
Price Index (CPI) Inflation and Core Inflation grew
below 3 percent in early 2020 until the end of 2021. This
reveals that the increase in the money supply (M2)
throughout 2020 to 2021, which was triggered by the
injection of large amounts of liquidity by Bank
Indonesia through the purchase of Government
Securities (SBN) for funding the State Budget, has been
considered for its impact on the inflation rate. An
increase in the money supply (M2) from January 2022
to early 2023 triggered by an increase in savings and
quasi-money in the community, as well as an increase in
bank credit, will increase the growth of CPI Inflation
and Core Inflation in August 2022 until early 2023.
3.2.3 Exchange rate
Figures 3.5 and 3.6 show that the variable exchange rate
to inflation Consumer Price Index (CPI) and Core
Inflation show the same response. CPI inflation and core
inflation tend to respond negative to shocks or changes
in exchange rate variables. CPI inflation responded
negative from the fourth to the last period with a shock
of -3.7 percent, while Core Inflation responded negative
from the third to the last period with a shock of -5.1
percent. The resulting negative response explains that
exchange rate movements with CPI Inflation and Core
Inflation are not in the same direction, where an increase
in the exchange rate will reduce the CPI Inflation and
Core Inflation rates, and conversely a decrease in the
exchange rate will increase the CPI Inflation and Core
Inflation rates. The results of the IRF analysis are
supported by the results of research by Lelo et al.,
(2018); Esprance and Fuling (2020); Laksamanany
(2022); Devia and Fadli (2022).
The relationship between the United States Dollar
exchange rate and the Indonesian Rupiah is expected to
be positive, but empirical results reveal different results,
namely CPI Inflation and Core Inflation tend to respond
negative to exchange rate variables, even though the
exchange rate of the United States Dollar to the
Indonesian Rupiah shows an increasing trend during the
Covid-19 Pandemic. An increase in the exchange rate of
the United States Dollar causes the value of the rupiah
to weaken (depreciate). According to the Mundell
Flemming Model, the depreciation of the value of the
domestic currency makes domestic goods cheaper than
goods abroad, thus stimulating net exports and total
income (Mankiw, 2019). The indirect relationship
between exchange rates and prices states that a decrease
in imports and an increase in exports increases net
external demand, which then increases total aggregate
demand and the inflation rate (Simorangkir & Suseno,
2004). In 2022 the growth rate of CPI Inflation and Core
Inflation will increase, in line with the weakening of the
rupiah exchange rate caused by the strengthening of the
United States Dollar.
The resulting negative response was due to Bank
Indonesia continuing to strengthen Rupiah exchange
rate stabilization measures to remain in line with its
fundamentals, amidst global financial market
uncertainties and efforts to control imported goods
Model I
Model II
Source: Eviews-12, 2023
(processed)
Figure 3.3
Results of IRF Analysis of CPI
Inflation on the Money Supply
Source: Eviews-12, 2023
(processed)
Figure 3.4
Results of Core Inflation IRF
Analysis on the Money Supply
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ĐĐƵŵƵůĂƚĞĚZĞƐƉŽŶƐĞŽĨ/,<ƚŽ/ϳZZ/ŶŶŽǀĂƚŝŽŶ
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ĐĐƵŵƵůĂƚĞĚZĞƐƉŽŶƐĞŽĨ/,<ƚŽ>EͺDϮ/ŶŶŽǀĂƚŝŽŶ
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ĐĐƵŵƵůĂƚĞĚZĞƐƉŽŶƐĞŽĨ/,<ƚŽ>Eͺ<hZ^/ŶŶŽǀĂƚŝŽŶ
ĐĐƵŵƵůĂƚĞĚZĞƐƉŽŶƐĞƚŽ ŚŽůĞƐŬLJKŶĞ ^;ĚĨĂĚũ ƵƐƚĞĚͿ/ŶŶŽǀĂƚŝŽŶƐ
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ĐĐƵŵƵůĂƚĞĚZĞƐƉŽŶƐĞŽĨ/Ed/ƚŽ>EͺDϮ/ŶŶŽǀĂƚŝŽŶ
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ϱϭϬ ϭϱ ϮϬ Ϯϱ ϯϬ ϯϱ
ĐĐƵŵƵůĂƚĞĚZĞƐƉŽŶƐĞŽĨ/Ed/ƚŽ>Eͺ<hZ^/ŶŶŽǀĂƚŝŽŶ
ĐĐƵŵƵůĂƚĞĚZĞƐƉŽŶƐĞƚŽ ŚŽůĞƐŬLJKŶĞ ^;ĚĨĂĚũ ƵƐƚĞĚͿ/ŶŶŽǀĂƚŝŽŶƐ
Model I
Model II
Source: Eviews-12, 2023
(processed)
Figure 3.5
Results of IRF Analysis of CPI
Inflation Against Exchange
Rates
Source: Eviews-12, 2023
(processed)
Figure 3.6
Results of IRF Analysis of Core
Inflation Against Exchange
Rates
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ĐĐƵŵƵůĂƚĞĚZĞƐƉŽŶƐĞƚŽ ŚŽůĞƐŬLJ KŶĞ^;Ě ĨĂĚũƵƐƚĞĚͿ/ŶŶŽǀĂƚŝŽŶƐ
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ĐĐƵŵƵůĂƚĞĚZĞƐƉŽŶƐĞŽĨ/Ed/ƚŽ/ϳZZ/ŶŶŽǀĂƚŝŽŶ
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ĐĐƵŵƵůĂƚĞĚZĞƐƉŽŶƐĞŽĨ/Ed/ƚŽ>EͺDϮ/ŶŶŽǀĂƚŝŽŶ
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ĐĐƵŵƵůĂƚĞĚZĞƐƉŽŶƐĞŽĨ/Ed/ƚŽ>Eͺ<hZ^/ŶŶŽǀĂƚŝŽŶ
ĐĐƵŵƵůĂƚĞĚZĞƐƉŽŶƐĞƚŽ ŚŽůĞƐŬLJ KŶĞ^;Ě ĨĂĚũƵƐƚĞĚͿ/ŶŶŽǀĂƚŝŽŶƐ
(15)
6
BIO Web of Conferences 146, 01042 (2024) https://doi.org/10.1051/bioconf/202414601042
BTMIC 2024
D(INTI) = 0.03259008037617∗ (INTI(−1)
−0.858299127253
∗LN_M2(−1)
−51.6868366824
∗LN_KURS(−1)
+507.085036362)
−0.0402467051129
∗(BI7DRR(−1)
−11.5383597313
∗LN_M2(−1)
−11.9090260007
∗LN_KURS(−1)
+293.320690786)
+0.44374993191
∗D(INTI(−1))
+0.290354326274
∗D(BI7DRR(1))
+0.525091134463
∗D(LN_M2(−1))
+0.967400736339
∗D(LN_KURS(−1))
(14)
3.2 Discussion
The results of the IRF analysis in this study are
presented in graphical form showing positive or
negative responses over 38 periods, according to the
time span of observations during the Covid - 19
Pandemic. The horizontal axis describes the time in the
next day after a shock occurs, while the vertical axis
describes the response value. The response generated in
the short term is usually quite significant and tends to
change, while in the long term it is consistent and tends
to shrink.
3.2.1 Interest rate
Figures 3.1 and 3.2 show that the variable policy interest
rates on inflation Consumer Price Index (CPI) and Core
Inflation show the same response. CPI inflation and core
inflation tend to respond negative to shocks or changes
in the policy interest rate variable. CPI inflation
responded negative from the third period to the last
period with the magnitude of the shock given -23.0
percent, while Core Inflation responded negative from
the sixth period to the last period with a shock amount
of -5.5 percent. The resulting negative response
explained that the movement of the policy interest rate
with CPI inflation and core inflation was not in the same
direction, where an increase in the policy interest rate
will reduce the CPI inflation rate and core inflation,
conversely, a decrease in the policy interest rate will
increase the CPI Inflation and Core Inflation rates. The
results of the IRF analysis are supported by the results
of research by Lelo et al., (2018); Esprance and Fuling
(2020); Junaeldi and Sentosa (2022); Ratri and
Munawar (2022).
The contraction in economic growth in the second
quarter of 2020 was -5.32 percent until the first quarter
of 2021 was -0.69 percent due to the Covid-19
pandemic, encouraging Bank Indonesia to implement
expansionary monetary policy in support of national
economic recovery, one of which is lowering policy
interest rates. In 2020 Bank Indonesia reduced the
policy interest rate five times with a total reduction of
125 bps to 3.75 percent at the end of the year, in 2021 it
will reduce the policy rate again by 25 bps to 3.50
percent. Changes in short-term interest rates are
transmitted to long-term interest rates in the supply and
demand side of the money market, which then affect the
cost of investment capital. A decrease in interest rates
makes the cost of new physical capital cheaper and
investments made profitable, resulting in an increase in
aggregate demand at a certain price level, thus shifting
the AD curve to the right (Mishkin, 2019). Lower
interest rates throughout 2020 and 2021 trigger an
increase in CPI inflation in early 2022 until it grows by
5.95 percent in September 2022.
Stock and Watson (2015) explain that policy interest
rates affect Core Inflation through the monetary policy
transmission mechanism, when the policy interest rate is
raised it will reduce aggregate demand in the economy,
conversely when the policy interest rate is lowered it
will encourage economic growth and increase the
inflation rate. The policy interest rate does not always
affect Core Inflation directly, but varies depending on
economic conditions and monetary policy. The
application of monetary policy influences the core
money supply, through open market operations. Core
money consists of currency circulating in the
community and balances in demand deposits owned by
commercial banks at Bank Indonesia. The central bank
raises interest rates to attract money in the community,
this will reduce the money supply and reduce inflation,
and vice versa, low interest rates will increase the money
supply and increase inflation (Tjahjono et al., 2000).
During the Covid – 19 Pandemic, the low monetary
policy interest rate continued to drive a reduction in
Model I
Model II
Source: Eviews-12, 2023 (processed)
Figure 3.1
Results of CPI Inflation IRF Analysis on
Policy Interest Rates
Source: Eviews-12, 2023 (processed)
Figure 3.2
Results of Core Inflation IRF Analysis on
Policy Interest Rates
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ĐĐƵŵƵůĂƚĞĚZĞƐƉŽŶƐĞ ƚŽŚŽůĞƐŬLJ KŶĞ^ ;ĚĨĂĚũƵƐƚĞĚͿ/ŶŶŽ ǀĂƚŝŽŶƐ
Source: Indonesian Economic and Financial Statistics (SEKI), 2023. (Data process ed)
Figure 4.7 Core Money Growth During the Covid-19 Pandemic
bank lending rates as an effort to facilitate and increase
financing (credit) for the business world. Asmadina et
al., (2021) revealed that lending had a positive and
significant effect on inflation in Indonesia during the
Covid - 19 Pandemic. The reduction in bank lending
rates has stimulated an increase in core money growth
as shown in Figure 4.6, in March 2020 core money
amounted to 1,031,285.12 billion rupiahs or grew by
3.56 percent to 1,544,963.73 billi