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A Bayesian VAR Approach to Short-Term Inflation Forecasting

Author

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  • Fethi Ogunc

Abstract

In this paper, we discuss the forecasting performance of Bayesian vector autoregression (BVAR) models for inflation under alternative specifications. In particular, we consider modelling in levels or in differences; choice of tightness; estimating BVARs of different model sizes and the accuracy of conditional and unconditional forecasts. Our empirical results point out that BVAR forecasts using variables in log-difference form outperform the ones using log-levels of the data. When we evaluate forecast performance in terms of model size, the lowest forecast errors belong to the models having relatively small number of variables, though we find only small difference in forecast accuracy among models of various sizes up to two quarter ahead. Finally, the conditioning seems to help to forecast inflation. Overall, pseudo evaluation findings suggest that small to medium size BVAR models having wisely selected variables in difference form and conditioning on the future paths of some variables appear to be a good choice to forecast inflation in Turkey.

Suggested Citation

  • Fethi Ogunc, 2019. "A Bayesian VAR Approach to Short-Term Inflation Forecasting," Working Papers 1925, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    • Handle: RePEc:tcb:wpaper:1925
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    File URL: https://www.tcmb.gov.tr/wps/wcm/connect/EN/TCMB+EN/Main+Menu/Publications/Research/Working+Paperss/2019/19-25
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    More about this item

    Keywords

    Inflation; Forecasting; Bayesian vector autoregression; Turkey;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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