Variable Selection and Inference for Multi-period Forecasting Problems

M Hashem Pesaran (), Andreas Pick () and Allan Timmermann ()

No CESifo Working Paper No. 2543, CESifo Working Paper Series from CESifo Group Munich

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.

Date: 2009
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Related works:
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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