Identifying good inflation forecaster
The objective of this paper is to identify the best indicator variable in forecasting inflation in Malaysia. Due to the fact that Malaysia experienced the rise of CPI by 4.8 percent in March 2006, the country’s highest inflation rate in seven years, there is a need to foresee future trend of general price level. To determine whether certain indicator (variable) could predict inflation, we construct a simple forecasting model that incorporates the variable. We estimate a two-variable VECM model of quasi-tradable inflation using monthly data covering the period 1980:01 to 2006:12. We alternate between the following inflation indicators: commodity prices, financial indicators and economic activities. We evaluate each model using out-of-sample forecast. The study proposes that a simple model using industrial production index improves the accuracy of inflation forecasts. The results support our hypothesis.
|Date of creation:||2008|
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- Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
- Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
- Michael F. Bryan & Stephen G. Cecchetti, 1993.
"The consumer price index as a measure of inflation,"
Federal Reserve Bank of Cleveland, issue Q IV, pages 15-24.
- Michael F. Bryan & Stephen G. Cecchetti, 1993. "The Consumer Price Index as a Measure of Inflation," NBER Working Papers 4505, National Bureau of Economic Research, Inc.
- Jonas D. M. Fisher, 2000. "Forecasting inflation with a lot of data," Chicago Fed Letter, Federal Reserve Bank of Chicago, issue Mar.
- Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
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