Beating a Random Walk: “Hard Times” for Forecasting Inflation in Post-Oil Boom Years?

Huseynov, Salman and Ahmadov, Vugar and Adigozalov, Shaig (2014): Beating a Random Walk: “Hard Times” for Forecasting Inflation in Post-Oil Boom Years?

Abstract

In this study, we investigate forecasting performance of various univariate and multivariate models in predicting inflation for different horizons. We design our forecast experiment for the post-oil boom years of 2010-2014 and compare forecasting ability of the different models with that of naïve ones. We find that for all forecast horizons simple naïve models have equal forecasting ability with relatively sophisticated models which allow for richer economic dynamics. To check whether forecasting ability of naïve models has not been inferior to relatively sophisticated ones in boom and pre-boom years as well, we repeat our forecast experiment and estimate the models for the period 2003-2006 and keep the years 2006-2010 for undertaking pseudo out-of-sample exercise. Our experiment reveals that surprising forecasting performance of naïve models in post-oil boom years is a new phenomenon and in fact, the employed models have exhibited significant forecasting advantage over naïve ones in boom and pre-boom years. We find that despite declining volatility in inflation over the post-oil boom years, it has become considerably difficult for our models to beat naïve ones due to recently unpredictable behavior of inflation.

• Item Type: MPRA Paper
• Language: English
• Keywords: Inflation; Forecasting; Time Series methods; Bayesian methods
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods

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