Variable Selection and Inference for Multi-period Forecasting Problems

Mohammad Pesaran, Allan Timmermann and Andreas Pick

No 7139, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: This paper conducts a broad-based comparison of iterated and direct multi-step forecasting approaches applied to both univariate and multivariate models. Theoretical results and Monte Carlo simulations suggest that iterated forecasts dominate direct forecasts when estimation error is a first-order concern, i.e. in small samples and for long forecast horizons. Conversely, direct forecasts may dominate in the presence of dynamic model misspecification. Empirical analysis of the set of 170 variables studied by Marcellino, Stock and Watson (2006) shows that multivariate information, introduced through a parsimonious factor-augmented vector autoregression approach, improves forecasting performance for many variables, particularly at short horizons.

Keywords: Forecast horizon; Macroeconomic forecasting; Factor-augmented var (search for similar items in EconPapers)
JEL-codes: C53 E27 (search for similar items in EconPapers)
Date: 2009-01
New Economics Papers: this item is included in nep-for and nep-mac
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Working Paper: Variable Selection and Inference for Multi-period Forecasting Problems (2009)
Working Paper: Variable Selection and Inference for Multi-period Forecasting Problems (2009)
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