Combination forecasts of output growth in a seven-country data set

James H. Stock, Mark W. Watson

Research output: Contribution to journalArticle

434 Scopus citations

Abstract

This paper uses forecast combination methods to forecast output growth in a seven-country quarterly economic data set covering 1959-1999, with up to 73 predictors per country. Although the forecasts based on individual predictors are unstable over time and across countries, and on average perform worse than an autoregressive benchmark, the combination forecasts often improve upon autoregressive forecasts. Despite the unstable performance of the constituent forecasts, the most successful combination forecasts, like the mean, are the least sensitive to the recent performance of the individual forecasts. While consistent with other evidence on the success of simple combination forecasts, this finding is difficult to explain using the theory of combination forecasting in a stationary environment.

Original languageEnglish (US)
Pages (from-to)405-430
Number of pages26
JournalJournal of Forecasting
Volume23
Issue number6
DOIs
StatePublished - Sep 1 2004

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Computer Science Applications
  • Strategy and Management
  • Statistics, Probability and Uncertainty
  • Management Science and Operations Research

Keywords

  • Forecast pooling
  • High-dimensional forecasting
  • Macroeconomic forecasting
  • Time-varying parameters

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