NOWCASTING INFLATION IN MALAYSIA
Muhammad Harunurashid Thelatha
DOI:
Keywords:
Nowcasting inflation, Mixed frequencies, Unrestricted Error Correction Model, Mixed Frequency Vector Autoregression, Mixed Data Sampling Regression
Abstract
The current study is intended to revisit the accuracy of inflation nowcasting using higher-than-monthly data sampling. We apply the Unrestricted Error Correction Model (UECM), Mixed Frequency Vector Autoregression (MFVAR) and Mixed Data Sampling Regression (MIDAS), chosen from a set of relevant predictors. The inflation nowcasting literature in Malaysia has focused on a univariate approach or single frequency; typically, monthly indicators are often used to estimate the current month accurately. This study utilises daily data (exchange rate and oil price) to generate more precise nowcasts of inflation for the current and subsequent months. In contrast to other existing methodologies, the methodology employed in this study offers the advantage of modelling all data inside a single unified framework while still permitting the generation of nowcasts for all variables involved. This provides the benefit of separating a model-based measure of “news” from each daily data release and subsequently evaluating its effect on the nowcast revision. The analysis indicates that the best model to nowcast inflation in the short-term is MIDAS, followed by MFVAR and UECM. In terms of RMSE, MAE, MAPE and Theil’s U-Statistic, the nowcasting accuracy decreases with nowcast length. Notwithstanding, focus should be placed on tackling the issue of price stability, which will help lower the production cost and minimise the pass-through effects, prior to early information obtained from the nowcasting framework.
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