Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models

Norman R. Swanson, Halbert White

Research output: Contribution to journalArticlepeer-review

137 Scopus citations

Abstract

Nine macroeconomic variables are forecast in a real-time scenario using a variety of flexible specification, fixed specification, linear, and nonlinear econometric models. All models are allowed to evolve through time, and our analysis focuses on model selection and performance. In the context of real-time forecasts, flexible specification models (including linear autoregressive models with exogenous variables and nonlinear artificial neural networks) appear to offer a useful and viable alternative to less flexible fixed specification linear models for a subset of the economic variables which we examine, particularly at forecast horizons greater than 1-step ahead. We speculate that one reason for this result is that the economy is evolving (rather slowly) over time. This feature cannot easily be captured by fixed specification linear models, however, and manifests itself in the form of evolving coefficient estimates. We also provide additional evidence supporting the claim that models which 'win' based on one model selection criterion (say a squared error measure) do not necessarily win when an alternative selection criterion is used (say a confusion rate measure), thus highlighting the importance of the particular cost function which is used by forecasters and 'end-users' to evaluate their models. A wide variety of different model selection criteria and statistical tests are used to illustrate our findings.

Original languageEnglish (US)
Pages (from-to)439-461
Number of pages23
JournalInternational Journal of Forecasting
Volume13
Issue number4
DOIs
StatePublished - Dec 1997
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Business and International Management

Keywords

  • Cointegration
  • Confusion rate
  • Linearity
  • Model selection
  • Nonlinearity
  • Parameter evolution
  • Real-time forecasting

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