## Abstract In this paper, we empirically evaluate competing approaches for combining inflation density forecasts in terms of Kullback–Leibler divergence. In particular, we apply a similar suite of models to four different datasets and aim at identifying combination methods that perform well throug
Forecasting inflation in Malaysia
✍ Scribed by Jarita Duasa; Nursilah Ahmad; Mansor H. Ibrahim; Mohd Pisal Zainal
- Publisher
- John Wiley and Sons
- Year
- 2010
- Tongue
- English
- Weight
- 427 KB
- Volume
- 29
- Category
- Article
- ISSN
- 0277-6693
- DOI
- 10.1002/for.1154
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✦ Synopsis
Abstract
This paper aims to identify the best indicator in forecasting inflation in Malaysia. In methodology, the study constructs a simple forecasting model that incorporates the indicator/variable using the vector error correction (VECM) model of quasi‐tradable inflation index and selected indicators: commodity prices, financial indicators and economic activities. For each indicator, the forecasting horizon used is 24 months and the VECM model is applied for seven sample windows over sample periods starting with the first month of 1980 and ending with the 12th month of every 2 years from 1992 to 2004. The degree of independence of each indicator from inflation is tested by analyzing the variance decomposition of each indicator and Granger causality between each indicator and inflation. We propose that a simple model using an aggregation of indices improves the accuracy of inflation forecasts. The results support our hypothesis. Copyright © 2009 John Wiley & Sons, Ltd.
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