Bulletin of Monetary Economics and Banking, Vol. 22, No. 4 (2019), pp. 423  436
FORECASTING INDONESIAN INFLATION WITHIN AN
DO
Solikin M. Juhro* and Bernard Njindan Iyke**
* Bank Indonesia Institute, Bank Indonesia
**Centre for Financial Econometrics, Deakin Business School, Deakin University, Melbourne, Australia. Email: Bernard@deakin.edu.au
ABSTRACT
We examine the usefulness of
Keywords: Forecasting inflation;
JEL Classification: E37.
Article history: 


Received 
: August 01, 2019 

Revised 
: November 16, 2019 

Accepted 
: December 20, 
2019 
Available online : December 31, 
2019 
https://doi.org/10.21098/bemp.v22i4.1235
424 
Bulletin of Monetary Economics and Banking, Volume 22, Number 4, 2019 


I. INTRODUCTION
We evaluate the performance of an inflation model consisting of a large set of exogenous predictors and lags of inflation against a simple model of inflation persistence for Indonesia within an
We evaluate both the in and
We find that the first lags of inflation, industrial production, import and export prices, global food prices, the global prices of agricultural raw materials, the money supply, the exchange rate between the Indonesian rupiah (IDR) and the US dollar (USD), consumption expenditures, and the unemployment rate are important predictors of inflation. In other words, 60% of the 15 exogenous predictors can forecast inflation for a PIP
Price stability is a core mandate of all central banks. Therefore, the prediction of inflation is always an important goal. The sheer volume of this literature rules out an exhaustive review. Older studies include those of Tzavalis and Wickens (1996), Stock and Watson (1999), Forni, Hallin, Lippi, and Reichlin (2003), and, more recently, Wright (2009), Koop and Korobilis (2012), Faust and Wright (2013),
Forecasting Indonesian Inflation Within an 
425 
Do 
and Chen, Turnovsky, and Zivot (2014), and Sharma (2019). These studies all use the Phillips curve (Stock and Watson, 1999) and its extensions to cover a broad range of financial and macroeconomic variables (Sharma, 2019) and estimation strategies (Forni, Hallin, Lippi, and Reichlin, 2003). However, as observed by Koop and Korobilis (2012), common issues affect various inflation forecasts, particularly those based on recursive regression. Structural changes shift model parameters upward or downward (Juhro, Narayan, Iyke, and Trisnanto, 2020). Such shifts, particularly those related to the coefficients, lead to time variation in the underlying relations, which are not well captured by recursive approaches. In addition, a variable’s predictive content can change over time, implying that the forecasting model for inflation can also change over time. Moreover, the number of inflation predictors can be large, leading to an even larger number of model combinations to estimate.
We contribute to the general literature by sidestepping these issues and using a DMA approach in forecasting inflation. The DMA approach allows time variation of the forecasting model and the coefficients in each model and accommodates different combination of models and predictors. Another contribution of our study is in response to the skewed focus of prior studies toward developed countries (e.g., Tzavalis and Wickens, 1996; Stock and Watson, 1999; Forni, Hallin, Lippi, and Reichlin, 2003; Stock and Watson, 2003; Wright, 2009; Koop and Korobilis, 2012; Faust and Wright, 2013; Chen, Turnovsky, and Zivot, 2014). Stock and Watson (2003), D’Agostino, Gambetti, and Giannone (2013), and Clark and Ravazzolo (2015), among other, consider the United States, while Caggiano, Kapetanios, and Labhard (2011), Giannone, Lenza, Momferatou, and Onorante (2014), and Berg and Henzel (2015), for example, consider developed European countries.
As noted by Sharma (2019), this is a problem for developing countries’ policymakers seeking to understand the evolution of inflation, in pursuit of price stability. Although our study and Sharma’s (2019) fill this research gap by developing forecasting models for a developing country, they differ in several ways: Sharma uses a bivariate predictive regression framework, which does not allow for time variation of the forecasting model and the coefficients in each model, nor can it accommodate different combinations of models and predictors. Ramakrishnan and Vamvakidis (2002), who assess the predictors of Indonesian inflation within a multivariate framework, have the same issue. The study closest to ours is that of Mandalinci (2017), who use
The Indonesian case is appealing because it is one of the few developing countries to have adopted a clear stance regarding effective policy coordination. The central bank, that is, Bank Indonesia, and the government now coordinate their policy deliberations and formulations (Juhro, Narayan, and Iyke, 2019), which became necessary in the aftermath of the 2007 global financial crisis (Juhro, 2015; Juhro and Goeltom, 2015). Central to this policy coordination is the mandate of achieving price stability under the Bank Indonesia Act of 1999, in growing recognition that both
426 
Bulletin of Monetary Economics and Banking, Volume 22, Number 4, 2019 


of the
Next, Section II presents the inflation forecasting model and the data. Section III presents the results. Section IV concludes the paper.
II.INFLATION FORECASTING MODEL AND DATA
A. Inflation Forecasting Model
The basic building block of all inflation forecasting models is the Phillips (1958) curve, which posits an inverse relation between wages and unemployment and, by extension, an inverse relation between inflation and unemployment (Samuelson and Solow, 1960). The theoretical implication of a negative relation between inflation and unemployment can be stated as
(1)
where πt, πte, μt, μtn, and σ are, respectively, the inflation rate, inflationary expectations, the unemployment rate, the natural rate of unemployment, and the model parameter (Ho and Iyke, 2019).
In practice, it is challenging to measure the natural rate of unemployment and inflationary expectations, because both variables are unobservable. Additionally, bidirectional causality is likely between unemployment and inflation, because they are jointly determined (Ho and Iyke, 2019). Two intuitions help us overcome these estimation challenges. First, the adaptive and rational expectation hypotheses indicate that inflation is persistent, and, second, hysteresis in unemployment indicates that
Stock and Watson (1999), among others, have suggested a generalized Phillips curve, which adds several predictors to the basic model. Following these studies, we can write the generalized Phillips curve as
(2)
Forecasting Indonesian Inflation Within an
Do 
427 




where πt is current inflation; 

is a set of predictors, including the first four lags 
of inflation; α and β are model parameters; and ϵt is the error term. The benchmark model (inflation persistence model) is Equation (2), but excluding the exogenous predictors of inflation.
Several issues can render forecasts based on Equation (2) inefficient or inaccurate. First, the model’s parameters (α and β) can change over time, due to structural changes in the economy, meaning the relations between inflation and its predictors can change over time. Second, the importance of each predictor can change over time, meaning that the forecasting model must change to adapt to this change. Third, there are large number of potential predictors of inflation, leading to an even larger number of model combinations to estimate. Given these issues, the recursive estimation of Equation (2) is less credible.
The DMA approach offers a credible solution to these issues. Let us assume a set of N models x(n) n=1,…,N associated with different subsets of predictors xt. Then, the set of models is
(3)
where 

and 

. Suppose 
that 

indicates 
the model 
that is 
used 
at each 
time period, 

and 
. 
Then, 
the 
DMA 
approach 
entails computing 
and averaging forecasts across models using these probabilities to forecast inflation at time t using inflation predictors through time
B. Data
We follow prior studies (Koop and Korobilis, 2012; Groen, Paap, and Ravazzolo, 2013) to gather the predictors of inflation. Most of the data are from Sharma (2019). Consistent with Sharma’s study, our measure of inflation (INF) is the monthly change in the Consumer Price Index. The 15 exogenous predictors are the logarithms of the industrial production index (LIP), the consumer confidence index (LCCI), the business confidence index (LBCI), the global price of food index (FOOD), the global price of agricultural raw material index (RAW), the Jakarta stock exchange capitalization (LCAP), the M2 money supply (LM2), the
428 
Bulletin of Monetary Economics and Banking, Volume 22, Number 4, 2019 


Table 1.
Definition of Variables
This table shows the variables, including their definition/construction, and their available dates. Majority of the data comes from Sharma (2019).
Variable 
Definition 
Date 
Source 

INF 
Change in consumer price index 
Sharma (2019) 

LIP 
Logarithm of industrial production index 
Sharma (2019) 

LCCI 
Logarithm of consumer confidence index 
Sharma (2019) 

LBCI 
Logarithm of Business confidence index 
Sharma (2019) 

IMPPI 
Import price index 
Sharma (2019) 

EXPPI 
Export price index 
Sharma (2019) 




Federal 

FOOD 
Logarithm of global price of food index (2016 = 100). 
Reserve 

Economic 








Data 




Federal 

RAW 
Logarithm of global price of agricultural raw material 
Reserve 


index (2016 = 100). 

Economic 




Data 

LCAP 
Logarithm of Jakarta stock exchange capitalization 
Sharma (2019) 

(value traded, USD). 





LM2 
Logarithm of M2 money supply. 
Sharma (2019) 

SPREAD 
Difference between 
Sharma (2019) 

month JIBOR. 




LER
LOIL
LNW
LCON
UEM
Logarithm of Indonesian rupiah per USD.
Logarithm of crude oil prices (West Texas
Intermediate USD per barrel).
Logarithm of average of net wage/salary per month of
employee, interpolated from annual data
Logarithm of total household consumption
expenditure.
Unemployment rate, interpolated from
data.
Federal
Economic
Data
National
Survey of
Indonesia
CIEC; Juhro
(2019b)
Global
Database
III. RESULTS
A. Summary Statistics
Table 2 shows the summary statistics of the variables. Our main statistic of interest is the unit root test, since it serves as guidance regarding how the variables should enter into the inflation forecasting model in Equation (2). We employ the widely used augmented
Forecasting Indonesian Inflation Within an 
429 
Do 
root is rejected for INF, LCCI, LBCI, SPREAD, and LNW at conventional statistical significance levels, implying that these variables are stationary and, therefore, enter into the model as levels. The remaining variables are not stationary and enter into the model as first differences. Note that we verify these results using the test of Narayan and Popp (2010, 2013). Table 3 reports the
Table 2.
Summary Statistics
The table shows summary statistics of the variables. The dependent variable is inflation (INF). The remaining variables are the predictors. Their definitions are in Table 1. SD, JB, and ADF, denote, respectively, standard deviation,
Variable 
Mean 
SD 
Skewness 
Kurtosis 
JB 
ADF(Lag) 
INF 
36.11 
40.87 
1.01 
2.60 
0.00 
4.07***(8) 
LIP 
12.58 
0.22 
0.21 
2.32 
0.02 

LCCI 
4.60 
0.01 
4.49 
0.00 

LBCI 
4.60 
0.01 
4.73 
0.00 

IMPPI 
0.78 
0.23 
2.01 
0.00 

EXPPI 
0.77 
0.22 
0.36 
1.85 
0.00 

FOOD 
4.43 
0.24 
0.29 
1.70 
0.00 

RAW 
4.52 
0.23 
0.45 
2.92 
0.00 

LCAP 
11.29 
1.33 
2.02 
0.00 

LM2 
14.92 
0.42 
2.25 
0.03 

SPREAD 
3.51 
34.61 
0.00 

LER 
6.50 
0.52 
1.40 
0.00 
0.34(12) 

LOIL 
3.55 
0.66 
0.31 
1.71 
0.00 

LNW 
13.33 
1.04 
1.79 
0.00 

LCON 
13.57 
0.32 
2.23 
0.02 

UEM 
5.50 
2.60 
0.33 
2.06 
0.00 
Table 3.
The table reports the



M1 




M2 



Variable 
Test statistic 
TB1 
TB2 
k 
Status 
Test 
TB1 
TB2 
k 
Status 

statistic 
























INF 
1999M06 
1999M07 
9 
I(0) 
1999M06 
2000M08 
9 
I(0) 

LIP 
1995M02 
2004M07 
12 
I(0) 
1995M02 
2001M05 
12 
I(0) 

LCCI 
2004M03 
2009M08 
12 
I(0) 
2003M02 
2004M03 
12 
I(0) 

LBCI 
2005M03 
2007M05 
12 
I(0) 
2007M05 
2008M06 
12 
I(0) 

IMPPI 
1995M04 
1996M05 
10 
I(0) 
1994M03 
1995M04 
10 
I(0) 

EXPPI 
1995M04 
1996M05 
12 
I(0) 
1996M05 
1997M06 
12 
I(0) 

FOOD 
1996M02 
2004M07 
4 
I(0) 
2004M07 
2005M07 
4 
I(0) 

RAW 
1995M03 
2004M07 
4 
I(0) 
2004M07 
2005M08 
4 
I(0) 

LCAP 
1994M02 
2005M08 
2 
I(0) 
2004M07 
2005M08 
2 
I(1) 

LM2 
2009M03 
2011M06 
2 
I(1) 
2009M03 
2010M05 
2 
I(1) 

SPREAD 
2009M03 
2011M06 
5 
I(0) 
2009M03 
2010M05 
5 
I(0) 

LER 
1998M04 
2002M06 
4 
I(0) 
1998M04 
2001M06 
4 
I(0) 

PP 
1998M05 
1999M05 
4 
I(0) 
1999M05 
2000M06 
4 
I(0) 

LOIL 
2000M05 
2004M08 
3 
I(0) 
1997M04 
2000M05 
3 
I(0) 

LNW 
2001M06 
2002M06 
4 
I(0) 
2002M06 
2003M07 
4 
I(0) 

LCON 
1997M02 
1998M03 
4 
I(0) 
1998M03 
1999M04 
4 
I(0) 

UEM 
1990M07 
1999M06 
2 
I(1) 
1990M07 
1999M06 
2 
I(1) 












430
2019 4, Number 22, Volume Banking, and Economics Monetary of Bulletin
Forecasting Indonesian Inflation Within an 
431 
Do 
B.
Having established how the variables enter into Equation (2), we prepare the model for estimation. Our benchmark model is a simple model of inflation persistence; that is, we regress inflation on the first four lags of inflation. Our generalized model follows prior studies (Koop and Korobilis, 2012; Groen, Paap, and Ravazzolo, 2013) and fits inflation as a function of the
Table 4 reports the DMA estimates of Equation (2). Following Iyke (2018), a predictor is said to forecast inflation if its PIP is approximately 0.50 (50%) or higher. Using this rule of thumb, we find that the first lags of inflation, industrial production, import and export prices, the global food price, the global prices of agricultural raw materials, the money supply, the
Prior studies (Ang, Bekaert, and Wei, 2007; Stock and Watson, 2008; Groen, Paap, and Ravazzolo, 2013) also find some or all of these predictors forecast inflation. Hence, our results are broadly consistent with the literature. From the Indonesian perspective, Ramakrishnan and Vamvakidis (2002) find the exchange rate and foreign inflation forecast inflation, while Sharma (2019) finds that business confidence, stock market capitalization, and the money supply are important predictors of inflation. Our estimates confirm their findings. We find that unemployment has a positive predictive impact on inflation, implying that high unemployment is followed by high inflation. This result violates the negative relation between inflation and unemployment posited by the Phillips curve. Our study is not the first to document that the relation between inflation and unemployment can be positive. For example, Ho and Iyke (2019) and Hooper, Mishkin, and Sufi (2019) show that the relation can be nonlinear. Specifically, these studies show a threshold beyond which the relation changes from negative to positive.
A number of reasons can explain an
432 
Bulletin of Monetary Economics and Banking, Volume 22, Number 4, 2019 


Phillips curve (Dupasquier and Rickets, 1998).1 An
Table 4.
The table reports the
Variable 
PM 
SD(PM) 
PIP 
SD(PIP) 
Constant 
0.97 
2.03 
1.00 
0.00 
0.63 
0.26 
0.61 
0.26 

0.12 
0.10 
0.28 
0.06 

0.08 
0.05 
0.23 
0.07 

0.10 
0.06 
0.21 
0.06 

0.03 
0.07 
0.50 
0.00 

0.19 
0.63 
0.40 
0.03 

0.18 
0.62 
0.40 
0.03 

0.12 
0.19 
0.49 
0.01 

0.17 
0.19 
0.48 
0.01 

0.31 
0.48 
0.01 

0.31 
0.47 
0.01 

0.28 
0.43 
0.03 

0.37 
0.37 
0.49 
0.00 

0.06 
0.31 
0.29 
0.11 

0.16 
0.18 
0.48 
0.01 

0.13 
0.34 
0.39 
0.06 

0.30 
0.32 
0.30 
0.02 

0.14 
0.22 
0.50 
0.00 

0.12 
0.30 
0.46 
0.04 
1Ball et al. (1988) provide a different explanation to convex Phillips curves. Juhro (2004) documents a convex Phillips curve for Indonesia.
Forecasting Indonesian Inflation Within an 
433 
Do 
C.
We set the
Table 5.
The table shows the
MSE 
PLD 

Panel A: h=1 
2.12 
134.94 

Panel B: h=5 
0.41 
386.44 

Panel C: h=9 
0.26 
524.67 


IV. CONCLUSION
We proposed a
434 
Bulletin of Monetary Economics and Banking, Volume 22, Number 4, 2019 


REFERENCES
Ang, A., Bekaert, G., & Wei, M. (2007). Do Macro Variables, Asset Markets, or Surveys Forecast Inflation Better?. Journal of Monetary Economics, 54,
Berg, T. O., & Henzel, S. R. (2015). Point and Density Forecasts for the Euro Area Using Bayesian VARs. International Journal of Forecasting, 31,
Ball, L., Mankiw, N. G., Romer, D., Akerlof, G. A., Rose, A., Yellen, J., & Sims, C. A. (1988). The New Keynesian Economics and the
Blanchard, O. J., & Summers, L. H. (1987). Hysteresis in Unemployment. European Economic Review, 31,
Caggiano, G., Kapetanios, G., & Labhard, V. (2011). Are More Data Always Better For Factor Analysis? Results for the Euro Area, The Six Largest Euro Area Countries and the UK. Journal of Forecasting, 30,
Camarero, M., Carrion‐i‐Silvestre, J. L., & Tamarit, C. (2006). Testing for Hysteresis In Unemployment in OECD Countries: New Evidence Using Stationarity Panel Tests with Breaks. Oxford Bulletin of Economics and Statistics, 68,
Catania, L., & Nonejad, N. (2018). Dynamic Model Averaging for Practitioners in Economics and Finance: The eDMA Package. Journal of Statistical Software, 84,
Chen, Y. C., Turnovsky, S. J., & Zivot, E. (2014). Forecasting Inflation Using Commodity Price Aggregates. Journal of Econometrics, 183,
Clark, T. E., & Ravazzolo, F. (2015). Macroeconomic Forecasting Performance Under Alternative Specifications of Time‐Varying Volatility. Journal of Applied Econometrics, 30,
D’Agostino, A., Gambetti, L., & Giannone, D. (2013). Macroeconomic Forecasting and Structural Change. Journal of Applied Econometrics, 28,
Dupasquier, C., & Ricketts, N. (1998).
Faust, J., & Wright, J. H. (2013). Forecasting Inflation. In Handbook of Economic Forecasting, 2, pp.
Fisher, T.C.G. (1989). Efficiency Wages: A Literature Survey. Working Paper
Forni, M., Hallin, M., Lippi, M., & Reichlin, L. (2003). Do Financial Variables Help Forecasting Inflation and Real Activity in the Euro Area?. Journal of Monetary Economics, 50,
Giannone, D., Lenza, M., Momferatou, D., & Onorante, L. (2014).
Gordon, R. J. (2013). The Phillips Curve is Alive and Well: Inflation and the NAIRU during the Slow Recovery. National Bureau of Economic Research (No. w19390)
Groen, J. J., Paap, R., & Ravazzolo, F. (2013).
Ho, S. Y., & Iyke, B. N. (2019). Unemployment and Inflation: Evidence of a Nonlinear Philips Curve in the Eurozone. The Journal of Developing Areas, 53. doi:10.1353/jda.2018.0077.
Forecasting Indonesian Inflation Within an 
435 
Do 
Hooper, P., Mishkin, F. S., & Sufi, A. (2019). Prospects for Inflation in a High Pressure Economy: Is the Philips Curve Dead or Is It Just Hibernating? (No. w25792). National Bureau of Economic Research.
Iyke, B. N. (2018). Macro Determinants of the Real Exchange Rate in A Small Open Small Island Economy: Evidence from Mauritius via BMA. Buletin Ekonomi Moneter dan Perbankan, 21,
Jaeger, A., & Parkinson, M. (1994). Some Evidence on Hysteresis in Unemployment Rates. European Economic Review, 38,
King, R. G., Stock, J. H., & Watson, M. W. (1995). Temporal Instability of the
Koop, G., & Korobilis, D. (2012). Forecasting Inflation Using Dynamic Model Averaging. International Economic Review, 53,
Juhro, S. M. (2004). Kurva Phillips dan Perubahan Struktural di Indonesia: Keberadaan, Pola Pembentukan Ekspektasi, dan Linieritas. Bulletin of Monetary Economics and Banking, 6,
Juhro, S. M. (2015). The Role of the Central Bank in Promoting Sustainable Growth: Perspectives on the Implementation of Flexible ITF in Indonesia. Afro Eurasian
Studies, 4,
Juhro, S. M., & Goeltom, M. S. (2015). Monetary policy regime in Indonesia. In
Juhro, S. M., & Iyke, B. N. (2019a). Monetary policy and financial conditions in Indonesia. Buletin Ekonomi Moneter dan Perbankan, 21,
Juhro, S. M., & Iyke, B. N. (2019b). Consumer confidence and consumption expenditure in Indonesia. Economic Modelling. https://doi.org/10.1016/j. econmod.2019.11.001
Juhro, S. M., Narayan, P. K., & Iyke, B. N. (2019). Understanding monetary and fiscal policy rule interactions in Indonesia. Working Paper.
Juhro, S. M., Narayan, P. K., Iyke, B. N., & Trisnanto, B. (2020). Is there a role for Islamic finance and R&D in endogenous growth models in the case of Indonesia? Working Paper (Under revision).
Mandalinci, Z. (2017). Forecasting Inflation in Emerging Markets: An Evaluation of Alternative Models. International Journal of Forecasting, 33,
Narayan, P. K., & Popp, S. (2010). A New Unit Root Test with Two Structural Breaks in Level and Slope at Unknown Time. Journal of Applied Statistics, 37,
Narayan, P. K., & Popp, S. (2013). Size and Power Properties of Structural Break Unit Root Tests. Applied Economics, 45,
Palley, T. I. (2003). The
Phillips, A. W. (1958). The Relation Between Unemployment and the Rate of Change of Money Wage Rates in the United Kingdom,
Raftery, A. E., Kárný, M., & Ettler, P. (2010). Online Prediction Under Model Uncertainty via Dynamic Model Averaging: Application to a Cold Rolling Mill. Technometrics, 52,
436 
Bulletin of Monetary Economics and Banking, Volume 22, Number 4, 2019 


Ramakrishnan, U., & Vamvakidis, A. (2002). Forecasting Inflation in Indonesia. IMF Working paper, No. 02/111.
Samuelson, Paul A.; Solow, Robert M. (1960). “Analytical Aspects of
Sharma, S. S. (2019). Variables Predict Indonesia’s Inflation?. Bulletin of Monetary Economics and Banking, 22,
Stiglitz, J. E. (1984). Price Rigidities and Market Structure. The American Economic Review, 74,
Stock, J. H., & Watson, M. W. (2008). Phillips Curve Inflation Forecasts (No. w14322). National Bureau of Economic Research.
Stock, J. H., & Watson, M. W. (1999). Forecasting Inflation. Journal of Monetary Economics, 44,
Stock, J. H., & W Watson, M. (2003). Forecasting Output and Inflation: The Role of Asset Prices. Journal of Economic Literature, 41,
Tzavalis, E., & Wickens, M. R. (1996). Forecasting Inflation from the Term Structure. Journal of Empirical Finance, 3,
Wright, J. H. (2009). Forecasting US Inflation by Bayesian Model Averaging. Journal of Forecasting, 28,