Time Series Forecasting using a Mixture of Stationary and Nonstationary Predictors

Sium Bodha Hannadige, Jiti Gao, Mervyn Silvapulle and Param Silvapulle

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Gross domestic product; high dimensional data; industrial production; macroeconomic forecasting; panel data (search for similar items in EconPapers)
JEL-codes: C13 C3 C32 C33 (search for similar items in EconPapers)
Date: 2021-01-30, Revised 2021-04-30
New Economics Papers: this item is included in nep-cwa, nep-ecm, nep-ets and nep-for
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Related works:
Working Paper: Time Series Forecasting Using a Mixture of Stationary and Nonstationary Predictors (2021)
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