Duasa, Jarita and Ahmad, Nursilah (2008): Identifying good inflation forecaster.
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Abstract
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.
Item Type: | MPRA Paper |
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Original Title: | Identifying good inflation forecaster |
Language: | English |
Keywords: | Goods inflation; VECM ; Malaysian economy |
Subjects: | C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C50 - General E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E31 - Price Level ; Inflation ; Deflation C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes |
Item ID: | 13302 |
Depositing User: | Jarita Duasa |
Date Deposited: | 10 Feb 2009 09:15 |
Last Modified: | 30 Sep 2019 16:40 |
References: | Bank Negara Malaysia (BNM), BNM Monthly Bulletin, various issues. Bryan, Michael F. and Cecchetti, Stephen G. 1993. “The Consumer Price Index as A Measure of Inflation,” Federal Reserve Bank of Cleveland Economic Review 29, No. 3. Cecchetti, Stephen G. et al. 2000. “The Unreliability of Inflation Indicators,” Current Issues in Economic and Finance, Vol. 6, No. 4. Federal Reserve Bank of New York. Dickey, D.A. and Fuller, W.A. 1987. “The Likelihood Ratio Statistics for Autoregressive Time series With a Unit Root,” Econometrica, 49(4): pp. 251-76. Fisher, Jonas D.M. 2000. “Forecasting Inflation With a Lot of Data,” Chicago Fed Letter, no. 151. March. Ibrahim, Mansor H. 1996. “Inflationary Behavior in Malaysia: An Empirical Re-Evaluation of its Determinants,” paper presented at 4th Malaysian Econometric Conference, Park Royal Hotel, Kuala Lumpur. 9-10 October, organized by MIER. Johansen, S. 1988. “Statistical Analysis of Cointegrating Vectors,” Journal of Economic Dynamics and Control, 12: pp. 231-254. Johansen, S. and Juselius, K. 1990. “Maximum Likelihood Estimation and Inference on Cointegration with Applications to the Demand for Money,” Oxford Bulletin of Economics and Statistics, 52(2): pp. 169-210. Phillips, P.C.B. and Perron, P. 1988. “Testing for a Unit Root in Time Series Regression,” Biometrika, 75(2): pp. 335-346. Stock, James H. and Watson, Mark W. 1999. “Forecasting Inflation,” NBER Working Paper No. 7023. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/13302 |