Variable Selection and Inference for Multi-period Forecasting Problems

Mohammad Pesaran, Andreas Pick and Allan Timmermann

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

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: 2009-01
New Economics Papers: this item is included in nep-ecm and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://files.econ.cam.ac.uk/repec/cam/pdf/cwpe0901.pdf Working Paper Version (application/pdf)

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)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:0901

Access Statistics for this paper

More papers in Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge
Bibliographic data for series maintained by Jake Dyer ().