Bulletin of Monetary Economics and Banking, Vol. 21, No. 3 (2019), pp. 283  302
MONETARY POLICY AND FINANCIAL
CONDITIONS IN INDONESIA
Solikin M. Juhro1, Bernard Njindan Iyke2
1Bank Indonesia Institute, Bank Indonesia, Jakarta, Indonesia. Email: solikin@bi.go.id 2 Centre for Financial Econometrics, Deakin Business School, Deakin University, Melbourne, Australia. Email: bernard@deakin.edu.au
ABSTRACT
We develop a financial condition index (FCI) and examine the effects of monetary policy on financial conditions in Indonesia. We show that our FCI tracks financial conditions quite well because it captures key financial events (the Asian financial crisis of
Keywords: Financial conditions; Monetary policy; Indonesia.
JEL Classifications: E44; E52.
Article history: 

Received 
: September 15, 2018 
Revised 
: January 2, 2019 
Accepted 
: January 4, 2019 
Available online : January 30, 2019
https://doi.org/10.21098/bemp.v21i3.1005
284Bulletin of Monetary Economics and Banking, Volume 21, Number 3, January 2019
I. INTRODUCTION
We create a new financial condition index (FCI) and analyse the effect of monetary policy on financial conditions in Indonesia. An FCI is a single indicator constructed to capture facets of the financial sector. Changing financial conditions are important for both policymakers and investors (Koop and Korobilis, 2014). Thus, a unique index to capture changing financial conditions has become popular in recent times. The debate on FCIs centres around what econometric approach and indicators of financial conditions should be used when constructing FCIs. For instance, Freedman (1994) contends that an FCI should capture exchange rate movements, whereas Dudley and Hatzius (2000) recommend the need for large scale macroeconomic indicators. In terms of approaches, two are mainly identified in the literature. The first, the
Among the earliest studies to construct FCIs are those of Goodhart and Hofmann (2001) and Mayes and Virén (2001), who note that house and stock prices are important drivers of financial conditions in the United Kingdom and Finland. Others, including Gauthier, Graham, and Liu (2004), Guichard and Turner (2008), and Swiston (2008), find corporate bond yield risk premiums and credit availability to be critical when constructing FCIs for Canada and the United States. FCIs have been extended to other economies, notably the Asian economies. Admittedly, the FCI literature in the Asian context is sparse. Studies such as those of Guichard, Haugh, and Turner (2009) and Shinkai and Kohsaka (2010) emphasize credit market conditions when constructing an FCI for Japan, while that of Osorio, Unsal, and Pongsaparn (2011) combine common factor and
We add to the limited studies on FCIs for Asian economies in the following ways. First, current studies construct FCIs using a panel of Asian countries (e.g. Osorio, Unsal, and Pongsaparn, 2011;
3The other two are South Korea, and Thailand.
Monetary Policy and Financial Conditions in Indonesia 
285 


the peak of the AFC (Iyke, 2018a). Agung, Juhro, and Harmanta (2016) argue that monetary policy alone is not sufficient to maintain macroeconomic stability and recommend complementary policies in Indonesia. In this regard, it is evident that understanding the evolution of the country’s financial conditions will go a long way in helping policymakers
Second, the impact of monetary policy on financial conditions in Indonesia and other Asian economies is poorly understood.
The main goal of monetary policy is to achieve macroeconomic and price (or monetary) stability. As argued by Juhro and Goeltom (2013), macroeconomic and price stability are tied to financial system stability in Indonesia because they are interlinked. Therefore, since financial conditions generally shape the direction of the economy (i.e. they serve as a leading indicator of business activities), our FCI would be a useful tool to enhance the decisions of participants in the Indonesian economy. We find that our FCI tracks financial conditions quite well. For instance, it captures the peaks of the AFC and the Indonesian banking crisis, the relatively stable period from 2000 until 2008, and the global financial crisis and its aftermath. This is consistent with previous FCIs. A unique feature of our FCI is that it is quarterly and thus offers near
The remainder of the paper is organized as follows. Section II presents the model specification and the data. Section III discusses the results. Section IV concludes the paper.
II.MODEL SPECIFICATION AND DATA A. Model Specification
This section outlines the approach used to construct the FCI. It also presents a
Vector Autoregressive (VAR) model to examine the effect of monetary policy on financial conditions.
4The price puzzle is a phenomenon whereby general prices react to a contractionary monetary policy shock by initially rising before falling (Sims, 1992). Christiano, Eichenbaum, and Evans (1999) recommend the inclusion of commodity prices to address this problem. The exchange rate puzzle arises when the exchange rate declines following a contractionary monetary policy shock (Cushman and Zha, 1997).
286Bulletin of Monetary Economics and Banking, Volume 21, Number 3, January 2019
A1. Dynamic Factor Model to Construct the FCI
We construct the FCI by employing a dynamic factor model. Given a set of endogenous variables (e.g. various indicators of economic and financial conditions), the dynamic factor model assumes that these variables are linear functions of certain unobserved factors and exogenous variables. The unobserved factors are therefore said to capture the movements of the set of endogenous variables. In theory, the unobserved factors and disturbances in the model are assumed to follow known correlation structures (Geweke, 1977; Stock and Watson, 1991). Following the literature (e.g. Geweke, 1977; Sargent and Sims, 1977), the following dynamic factor model can be specified:
(1)
(2)
(3)
where y is a vector of dependent variables, f is a vector of unobservable factors, x and w are vectors of exogenous variables, u, v, and ϵ are vectors of disturbances, P, Q, and R are matrices of parameters, A and C are matrices of autocorrelation parameters, and t, p, and q are time and lag subscripts, respectively.
In our application, y contains the indicators of financial conditions (exchange rate, credit, interest rates, equity indices, and business conditions). These indicators are modelled as linear functions of unobserved factors assumed to follow a second order autoregressive process, to capture persistence in financial conditions. The FCI is the predicted vector of unobservable factors f̂(a
A2. VAR Model for the Indonesian Economy
We link monetary policy to financial conditions by estimating the following VAR model for the Indonesian economy:
, 
(4) 
where Yt is an n×1 vector of macroeconomic indicators (i.e. real output, consumer price index, FCI, commodity prices, Treasury bill rate, etc.), βi is an n×n parameter matrix, ut is the
5In application, maximum likelihood is implemented in two steps. In the first step, the model is presented in
Monetary Policy and Financial Conditions in Indonesia 
287 


The policy shock is identified through the
(5)
Equation (5) indicates
B. Data
Our sample covers the period 1994: Q1 to 2018: Q4. To construct the FCI, we use various variables indicating specific aspects of the financial conditions in Indonesia. We use Bank Indonesia’s rate (IRATE)7 for the interest rate channel, the nominal effective exchange rate (NER) for the exchange rate channel, banking system claims on private enterprise (CREDIT) for the credit channel, the Jakarta Composite Index (JCI) and the MSCI Share Price Index (MSCI) for the equity channel, and the business confidence index (BCI) and the consumer confidence index (CCI) for the expectation or perception channel. In the VAR model, we use the manufacturing production index (MP), the growth in CPI, the FCI, the commodity price index (COM), NER, the
6See, for example, Bernanke (1986), Blanchard and Watson (1986), Blanchard and Quah (1989), Uhlig
(2005), and
7Note that, since 2005 (under the inflation targeting framework), Bank Indonesia has used different policy rates. From 2005 until
288Bulletin of Monetary Economics and Banking, Volume 21, Number 3, January 2019
III. RESULTS
A. Measuring Financial Conditions
We begin our analysis by testing for unit roots in the indicators of financial conditions. These results are shown in Table 1. There is no strong evidence to reject the unit root null hypothesis. Therefore, we proceed to constructing the FCI by modelling the indicators in their first differences as linear functions of an unobserved factor. The unobserved factor is assumed to follow a
Table 1.
Tests for Unit Roots in FCI Constituents
This table reports unit root test results based on the Augmented

ADF test 
PV test 

Variable 
Innovation outlier 

Constant 
Constant and 
Break date 




Trend 






IRATE 
2008M10 

lnBCI 
2009M01 

CCI 
2002M02 

lnCREDIT 
2000M08 

lnNER 
1997M07 

lnJCI 
2003M03 

lnMSCI 
1998M07 
Table 2 shows the maximum likelihood estimates of the dynamic factor model. Because two of the constituents of the FCI, the business confidence index (BCI) and the consumer confidence index (CCI) have a short time span (i.e. they start in 2000:Q1, whereas the others start in 1994:Q1), we estimate the dynamic factor model with and without these variables. The seven variables used for the dynamic factor model are IRATE, NER, CREDIT, JCI, MSCI, BCI, and CCI. Model
(1)contains all seven variables, whereas model (2) contains all seven except for BCI and CCI. Both models generally indicate some degree of persistence in the unobserved factor, since immediate past values of the factor are significant in the model. The unobserved factor appears to be a significant predictor of all indicators except CREDIT in model (1). The factors have less predictive power over NER, CREDIT, and MSCI in model (2). The estimated signs of the coefficients are generally consistent with conventional wisdom; that is, we could infer that high interest rates tend to signal bad financial conditions, an appreciating rupiah exchange rate signals good financial conditions, high equity returns signal good financial conditions, and good business conditions (perceptions and expectations) translate to good financial conditions.
Monetary Policy and Financial Conditions in Indonesia 
289 


Table 2.
Dynamic Factor Estimates
This table reports estimates of the dynamic factor model. The constituents of the FCI are specified in their
Variables 
Coefficient 

Factor 
Model (1) 
Model (2) 
Lag 1 
0.432***[3.650] 
0.507**[2.230] 
Lag 2 

∆IRATE 

∆lnNER 
0.014***[3.490] 
0.005[1.590] 
∆lnCREDIT 
0.010[0.290] 

∆lnJCI 
0.108***[11.750] 
0.018***[4.740] 
∆lnMSCI 
0.021*[1.820] 

∆lnBCI 
0.017**[2.090] 

∆CCI 
1.964*[1.820] 

Log likelihood 
1503.705 

Wald 
152.100 
410.560 
Prob > 
0.000 
0.000 
Number of observations 
69 
296 
Sample 
2001Q2 – 2018Q4 
1994Q1 – 2018Q4 
Figure 1 shows the extracted FCI values plotted against changes in interest rates and Figure 2 shows only the FCIs.8 The period between 1997 and 2002 was turbulent. Financial conditions worsened between 1997 and 1998, which were the peaks of the AFC and the Indonesian banking crisis (Iyke, 2018a). This time is followed by enhanced financial conditions between 1998 and 1999, a sharp decline between 1999 and 2001, and subsequent improvement between 2001 and 2002. Beyond this deterioration and recovery phase, financial conditions were moderate and stable in the country until a marked decline and subsequent recovery between 2008 and 2010. The fluctuations in our FCI look a bit similar to those of the annual FCI developed by
8The FCI with BCI and CCI appears to be smaller in absolute terms than the FCI without these two variables. The former captures the key FCI determinants and is therefore a more accurate indicator of financial conditions in the country than the latter.
290Bulletin of Monetary Economics and Banking, Volume 21, Number 3, January 2019
Figure 1. FCI Movement
This graph shows the movements of the FCI (with and without BCI and CCI) and interest rates (1994: Q1 to 2018: Q4).
30 





20 





10 





0 






Interest rate growth 
FCI without BCI and CCI 






FCI with BCI and CCI 









1995q1 
2000q1 
2005q1 
2010q1 
2015q1 
2020q1 
Figure 2. FCI for Indonesia
This graph shows the movements of the FCI for Indonesia (1994: Q1 to 2018: Q4).
10 





5 





0 












FCI with BCI and CCI 
FCI without BCI and CCI 








1995q1 
2000q1 
2005q1 
2010q1 
2015q1 
2020q1 
B. Impact of Monetary Policy on Financial Conditions
Financial conditions are not independent of monetary policies. The actions of monetary authorities tend to shape financial conditions. For instance, a tight monetary policy leads to credit shrinkage in the economy. This, in turn, leads to firms cutting down production, layoffs, declines in demand for goods and services, and reductions in business confidence. Similarly, an expansionary monetary policy leads to expansions in credit, production, employment, the demand for goods and services, and inflationary pressures, among others. Good financial conditions, if
Monetary Policy and Financial Conditions in Indonesia 
291 


not properly safeguarded, can implode, owing to excessive speculative activities and lack of due diligence, especially in the area of credit allocation. The recent global financial crisis was mainly triggered by these factors.
In this section, we explore how financial conditions respond to monetary policy shocks (or surprises). In other words, we analyse how financial conditions respond to a sudden monetary policy contraction or expansion. We identify a monetary policy shock as an innovation in the
(6)
In addition to imposing lower triangularity on A in equation (5), we impose on (B,Σ) a flat normal
The resulting graph is shown in Figure 3. A contractionary monetary policy shock leads to unfavourable financial conditions (a decline in FCI below zero) one quarter after the shock. This deterioration in financial conditions persists until the end of the second quarter. Financial conditions improve (FCI rises above zero) for nearly three quarters before declining. We track the robustness of the FCI response to contractionary monetary policy by obtaining IRFs from an alternative ordering strategy. In this case, STR is ordered second but last. This identification is motivated by previous studies (e.g., Christiano, Eichenbaum, and Evans, 1999; Uhlig, 2005), which argue that monetary policy has an immediate effect only on the policy rate
(7)
The graph for this strategy is shown in Figure 4. The IRF following a contractionary shock is qualitatively the same as that in Figure 3. Our findings are consistent with those of Satria and Juhro (2011), who document a strong impact of the monetary policy stance on financial sector policies. They document a consistent procyclical relationship between risk and
9Canova (2007) provides technical details on this prior restriction.
10We impose two lags because of considerations of sample size and degree of freedom.
292Bulletin of Monetary Economics and Banking, Volume 21, Number 3, January 2019
note that such a relationship tends to reverse the impact of expansionary monetary policy. We document that expansionary monetary policy is linked with favourable financial conditions for the first few quarters. In the medium term, our findings appear to corroborate theirs, in that financial conditions appear to decline, perhaps due to the reduction in
Figure 3. Response of FCI to Monetary Policy Shocks
This figure shows the response of financial conditions to a contractionary monetary policy shock of one standard deviation in size, which is identified as the innovation in the
Response to Cholesky One S.D. Innovations ± 2 S.E.
1,2 









0,8 









0,4 









0,0 





























1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
Figure 4. Response of FCI to Monetary Policy Shocks – Alternative Ordering
This figure shows the response of financial conditions to a contractionary monetary policy shock of one standard deviation in size, which is identified as the innovation in the
Response to Cholesky One S.D. Innovations ± 2 S.E.
,8 









,6 









,4 









,2 









,0 





























1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
Monetary Policy and Financial Conditions in Indonesia 
293 


IV. CONCLUSION
We create a new FCI and analyse the effect of monetary policy on financial conditions in Indonesia. There are, so far, only limited FCI studies on Asian economies. These studies are based on a panel of Asian economies; however, these countries are interlinked through trade and, therefore, analysis of the unique attributes of their FCIs becomes highly tasking within a single framework. We address this issue by solely focusing on Indonesia.
Indonesia has undergone substantial changes in terms of financial conditions, making it appealing for this study. The country is among the three that were most affected by the AFC. It has also, in recent times, experienced the sharpest depreciation in its currency since the peak of the AFC. Good FCIs would enhance authorities’ abilities to
We find that our FCI tracks financial conditions quite well. For instance, it captures the peaks of the AFC and the Indonesian banking crisis, the relatively stable period from 2000 until 2008, and the global financial crisis and its aftermath. This is consistent with previous FCIs. A unique feature of our FCI is that it is quarterly and thus offers near
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Bernanke, B. S. (1986). Alternative Explanations of the
Blanchard, O., & Watson, M. (1986). Are All Business Cycles Alike? In: Gordon, R.J. (Ed.), The American Business Cycle. University of Chicago Press, Chicago,
Blanchard, O. J., & Quah, D. (1989). The Dynamic Effects of Aggregate Demand and Supply Shocks. American Economic Review, 79,
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Brave, S., & Butters, R. (2011). Monitoring Financial Stability: A Financial Conditions Index Approach. Economic Perspectives, 35,
Canova, F. (2007). Methods for Applied Macroeconomic Research. Princeton, NJ: Princeton University Press.
Christiano, L., Eichenbaum, M., & Evans, C. (1999). Monetary Policy Shocks: What Have I Learned and to What End? In: Woodford, M., Taylor, J.B. (Eds.), Handbook of Macroeconomics.
Cushman, D. O., & Zha, T. (1997). Identifying Monetary Policy in a Small Open Economy Under Flexible Exchange Rates. Journal of Monetary Economics, 39,
Dudley, W., & Hatzius, J. (2000). The Goldman Sachs Financial Conditions Index: The Right Tool for a New Monetary Policy Regime. Global Economics Paper No. 44.
Freedman, C. (1994). The Use of Indicators and of the Monetary Conditions Index in Canada. Frameworks for monetary stability: policy issues and country experiences,
Gauthier, C., Graham, C., & Liu, Y. (2004). Financial Conditions Indexes for Canada. Bank of Canada Working Paper No.
Geweke, J. (1977). The Dynamic Factor Analysis of Economic Time Series Models. In Latent Variables in Socioeconomic Models, ed. D. J. Aigner & A. S. Goldberger,
Goldstein, M. (1998). The Asian Financial Crisis: Causes, Cures, and Systemic Implications. Peterson Institute, 55.
Goodhart, C., & Hofmann, B. (2001). Asset Prices, Financial Conditions, and The Transmission of Monetary Policy. In conference on Asset Prices, Exchange Rates, and Monetary Policy (March, 2001), Stanford University
Guichard, S., & Turner, D. (2008). Quantifying the Effect of Financial Conditions on US Activity. OECD Economics Department Working Paper No. 635.
Guichard, S., Haugh, D., & Turner, D. (2009). Quantifying the Effect of Financial Conditions in the Euro Area, Japan, United Kingdom, and United States. OECD Economics Department Working Paper No. 677.
Iyke, B.N. (2018a) A Test of the Foreign Exchange Market Efficiency in Indonesia. Bulletin of Monetary Economics and Banking (forthcoming).
Iyke, B. N. (2018b). Assessing the Effects of Housing Market Shocks on Output: The Case of South Africa. Studies in Economics and Finance, 35,
Juhro, S. M., & Goeltom, M. (2013) The Monetary Policy Regime in Indonesia (November 1, 2013).
Koop, G., & Korobilis, D. (2014). A New Index of Financial Conditions. European Economic Review, 71,
Mayes, D., & Virén, M. (2001). Financial Conditions Indexes. Bank of Finland Discussion Paper No.
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Satria, D., & Juhro, S. M. (2011). Risk Behavior in the Transmission Mechanism of Monetary Policy in Indonesia. Bulletin of Monetary Economics and Banking, 13,
Sargent, T. J., & Sims,C. A. (1977). Business Cycle Modeling without Pretending to Have too much a Priori Economic Theory. In New Methods in Business Cycle Research: Proceedings from a Conference, ed. C. A. Sims,
Shinkai,
Sims, C. A. (1986). Are Forecasting Models Usable for Policy Analysis. Minneapolis Federal Reserve Bank Quarterly Review Winter,
Sims, C. A. (1992). Interpreting the Macroeconomic Time Series Facts. European Economic Review, 36,
Stock, J. H., & Watson, M. W. (1991). A Probability Model of the Coincident Economic Indicators. In Leading Economic Indicators: New Approaches and Forecasting Records, ed. K. Lahiri and G. H. Moore,
Swiston, A. (2008). A U.S. Financial Conditions Index: Putting Credit where Credit is Due. IMF Working Paper No. 08/161.
Osorio, M. C., Unsal, D. F., & Pongsaparn, M. R. (2011). A Quantitative Assessment of Financial Conditions in Asia (No.
Perron, P., & Vogelsang, T. J. (1992). Nonstationarity and Level Shifts with an Application to Purchasing Power Parity. Journal of Business & Economic Statistics, 10,
Uhlig, H. (2005). What are the Effects of Monetary Policy on Output? Results from an Agnostic Identification Procedure. Journal of Monetary Economics, 52, 381 419.
Yamazawa, I. (1998). The Asian Economic Crisis and Japan. The Developing Economies, 36,
296Bulletin of Monetary Economics and Banking, Volume 21, Number 3, January 2019
APPENDIX
Figure A1. Variables Used for Constructing FCI and the VAR Model
This figure shows the behaviour of the variables used in constructing the FCI and the VAR model. The first seven graphs are the financial condition indicators used in the FCI model. The last seven (including lnNER) graphs are those variables used in the VAR model to examine the impact of monetary policy shocks on financial conditions. The maximum sample period employed is from 1994: Q1 to 2018: Q4.
80 

Interest Rate 







60 




40 




20 




0 




1995 
2000 
2005 
2010 
2015 
Log of Business Conﬁdence Index
5,0
4,8
4,6
4,4
4,2
4,0
1995 
2000 
2005 
2010 
2015 
Monetary Policy and Financial Conditions in Indonesia 
297 


Figure A1. Variables Used for Constructing FCI and the VAR Model (Continued)
30
Consumer Conﬁdence Index
20 




10 




0 














1995 
2000 
2005 
2010 
2015 
9 

Log of Jakarta Composite Index 







8 




7 




6 




5 




1995 
2000 
2005 
2010 
2015 
6,5 

Log of Nominal Eﬀective Exchange Rate 







6,0 




5,5 




5,0 




4,5 




4,0 




1995 
2000 
2005 
2010 
2015 
298Bulletin of Monetary Economics and Banking, Volume 21, Number 3, January 2019
Figure A1. Variables Used for Constructing FCI and the VAR Model (Continued)
Log of Credit to Private Sector
12 




10 




8 




6 




4 




1995 
2000 
2005 
2010 
2015 
Log of MSCI Index
9 




8 




7 




6 




5 




1995 
2000 
2005 
2010 
2015 
12 


FCI 






8 




4 




0 














1995 
2000 
2005 
2010 
2015 
Monetary Policy and Financial Conditions in Indonesia 
299 


Figure A1. Variables Used for Constructing FCI and the VAR Model (Continued)
4,8 

Log of Manufacturing Production Index 







4,6 




4,4 




4,2 




4,0 




3,8 




1995 
2000 
2005 
2010 
2015 
5,0 

Log of CPI 







4,5 




4,0 




3,5 




3,0 




2,5 




1995 
2000 
2005 
2010 
2015 


Log of Commodity Price Index 

9,0 




8,5 




8,0 




7,5 




7,0 




6,5 




1995 
2000 
2005 
2010 
2015 
300Bulletin of Monetary Economics and Banking, Volume 21, Number 3, January 2019
Figure A1. Variables Used for Constructing FCI and the VAR Model (Continued)
Log of Nominal Eﬀective Exchange Rate
6,5 




6,0 




5,5 




5,0 




4,5 




4,0 




1995 
2000 
2005 
2010 
2015 
60 

Monetary Policy Rate 







50 




40 




30 




20 




10 




0 




1995 
2000 
2005 
2010 
2015 
Log of M2
16 




15 




14 




13 




12 




11 




1995 
2000 
2005 
2010 
2015 
Table A1.
Summary Statistics of the Variables
This table shows the summary statistics of the variables used in constructing the FCI and the VAR model. The first seven variables are the financial condition indicators used in the FCI model. The last six variables (including lnNER) are those used in the VAR model to examine the impact of monetary policy shocks on financial conditions. Details on these variables are shown in Table A2 below. The maximum sample period employed is from 1994: Q1 to 2018: Q4.

IRATE 
lnBCI 
CCI 
lnJCI 
lnNER 
lnCREDIT 
lnMSCI 
FCI 
lnMP 
lnCPI 
lnCOM 
MPR 
lnM2 














Mean 
11.4595 
4.6168 
9.3669 
7.2689 
4.8170 
8.4910 
7.9132 
4.3013 
3.8927 
7.7550 
11.8368 
14.0364 

Median 
8.2500 
4.6697 
11.2094 
7.1833 
4.6568 
8.9695 
8.4273 
4.2633 
4.0636 
7.8521 
8.1088 
14.0408 

Maximum 
63.9867 
4.8296 
21.9900 
8.774 
6.0981 
10.5924 
8.9346 
8.9801 
4.7152 
4.7121 
8.6577 
55.9093 
15.5501 
Minimum 
4.2500 
4.1395 
5.8908 
4.2186 
5.0304 
5.9829 
3.9158 
2.5649 
6.7893 
4.3788 
11.9129 

Std. Dev. 
9.4723 
0.1653 
7.8302 
1.0123 
0.5439 
1.8596 
0.9587 
1.8880 
0.2021 
0.6530 
0.6207 
9.4480 
1.0258 
Skewness 
3.5915 
0.0939 
1.4211 
1.1774 
0.3713 
2.8066 

Kurtosis 
17.5034 
2.9748 
2.9952 
1.3643 
3.7314 
2.2045 
2.0083 
12.5296 
2.2989 
2.2684 
1.4112 
11.2036 
2.2016 
1091.4360 
8.9732 
6.5492 
11.2948 
35.8855 
13.0210 
13.5779 
397.4757 
4.2152 
9.4113 
10.7835 
358.1764 
4.8051 

Probability 
0.0000 
0.0113 
0.0378 
0.0035 
0.0000 
0.0015 
0.0011 
0.0000 
0.1215 
0.0090 
0.0046 
0.0000 
0.0905 
Sum 
1145.945 
327.7949 
711.8835 
726.8904 
481.6957 
840.6125 
791.3154 
417.2214 
385.3742 
767.7429 
1029.8010 
1403.639 

Sum Sq. Dev. 
8882.657 
1.9130 
4598.427 
101.4415 
29.2839 
338.9129 
90.9972 
349.3148 
3.9221 
41.7869 
37.7557 
7676.6940 
104.1749 
Observations 
100 
71 
76 
100 
100 
99 
100 
99 
97 
99 
99 
87 
100 














Indonesia in Conditions Financial and Policy Monetary
301
Table A2.
Details on the Variables
This table shows details on the variables used in constructing the FCI and the VAR model. The first seven variables are the financial indicators used in the FCI model. The last six variables (including lnNER) are those used in the VAR model to examine the impact of monetary policy shocks on financial conditions. The maximum sample period employed is from 1994: Q1 to 2018: Q4.
Indicator 
Variable 
Period 
Source 




IRATE 
Interest rate proxied by the Bank Indonesia (BI) rate (From July 2005 to July 2016, we use ‘implicit rate’ 
1994Q1 – 2018Q4 
Bloomberg 

anchoring to 



policy rate does not change the stance of BI monetary policy as old rate and new rate are in the same term 



structure (different tenors) 


lnBCI 
Logarithm of the business confidence index (Business Activity Survey) 
2000Q1 – 2018Q4 
Statistics Indonesia 
CCI 
Consumer confidence index 
2001Q2 – 2018Q4 
Statistics Indonesia 
lnJCI 
Logarithm of the Jakarta Composite Price Index 
1994Q1 – 2018Q4 
Bloomberg 
lnNER 
Logarithm of the nominal effective exchange rate 
1994Q1 – 2018Q4 
Bloomberg 
lnCREDIT 
Logarithm of the banking system: claims on private sector 
1994Q1 – 2018Q3 
Bloomberg 
lnMSCI 
Logarithm of the MSCI Share Price Index 
1994Q1 – 2018Q4 
Bloomberg 
FCI 
Financial condition index computed as using dynamic factor of above variables 
1994Q1 – 2018Q3 
Computed 
lnMP 
Logarithm of the total manufacturing production for Indonesia (2015 =100) 
1994Q1 – 2018Q1 
Federal Reserve 



Bank of St. Louis 
lnCPI 
Logarithm of the consumer price index 
1994Q1 – 2018Q3 
Federal Reserve 



Bank of St. Louis 
lnCOM 
Logarithm of the commodity price index computed as PCA of crude oil, natural gas Index (2010=100), 
1994Q1 – 2018Q3 
Word Bank 

copper, and gold 


MPR 
Monetary policy rate proxied by 
1997Q2 – 2018Q4 
Bloomberg 
lnM2 
Logarithm of money supply (M2) 
1994Q1 – 2018Q4 
Bloomberg 




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2019 January 3, Number 21, Volume Banking, and Economics Monetary of Bulletin