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Variable selection, estimation and inference for multi-period forecasting problems

Authors
Journal
Journal of Econometrics
0304-4076
Publisher
Elsevier
Publication Date
Volume
164
Issue
1
Identifiers
DOI: 10.1016/j.jeconom.2011.02.018
Keywords
  • Direct Forecasts
  • Iterated Forecasts
  • Factor- Augmented Vars
  • Sure Estimation
  • Akaike Information Criterion
Disciplines
  • Economics

Abstract

Abstract This paper conducts a broad-based comparison of iterated and direct multi-period forecasting approaches applied to both univariate and multivariate models in the form of parsimonious factor-augmented vector autoregressions. To account for serial correlation in the residuals of the multi-period direct forecasting models we propose a new SURE-based estimation method and modified Akaike information criteria for model selection. Empirical analysis of the 170 variables studied by Marcellino, Stock and Watson (2006) shows that information in factors helps improve forecasting performance for most types of economic variables although it can also lead to larger biases. It also shows that SURE estimation and finite-sample modifications to the Akaike information criterion can improve the performance of the direct multi-period forecasts.

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