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Time Series Forecasting Using a Mixture of Stationary and Nonstationary Predictors

Author

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  • Sium Bodha Hannadige
  • Jiti Gao
  • Mervyn J Silvapulle
  • Param Silvapulle

Abstract

We develop a method for constructing prediction intervals for a nonstationary variable, such as GDP. The method uses a factor augmented regression [FAR] model. The predictors in the model includes a small number of factors generated to extract most of the information in a set of panel data on a large number of macroeconomic variables considered to be potential predictors. The novelty of this paper is that it provides a method and justification for a mixture of stationary and nonstationary factors as predictors in the FAR model; we refer to this as mixture-FAR method. This method is important because typically such a large set of panel data, for example the FRED-MD, is likely to contain a mixture of stationary and nonstationary variables. In our simulation study, we observed that the proposed mixture-FAR method performed better than its competitor that requires all the predictors to be nonstationary; the MSE of prediction was at least 33% lower for mixture-FAR. Using the data in FRED-QD for the US, we evaluated the aforementioned methods for forecasting the nonstationary variables, GDP and Industrial Production. We observed that the mixture-FAR method performed better than its competitors.

Suggested Citation

  • Sium Bodha Hannadige & Jiti Gao & Mervyn J Silvapulle & Param Silvapulle, 2021. "Time Series Forecasting Using a Mixture of Stationary and Nonstationary Predictors," Monash Econometrics and Business Statistics Working Papers 6/21, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2021-6
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    gross domestic product; high dimensional data; industrial production; macroeconomic forecasting; panel data;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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