Predictive inference under model misspecification

Nii Ayi Armah, Norman R. Swanson

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

Abstract

In this chapter we discuss model selection and predictive accuracy tests in the context of parameter and model uncertainty under recursive and rolling estimation schemes. We begin by summarizing some recent theoretical findings, with particular emphasis on the construction of valid bootstrap procedures for calculating the impact of parameter estimation error. We then discuss the Corradi and Swanson (2002) (CS) test of (non)linear out-of-sample Granger causality. Thereafter, we carry out a series of Monte Carlo experiments examining the properties of the CS and a variety of other related predictive accuracy and model selection type tests. Finally, we present the results of an empirical investigation of the marginal predictive content of money for income, in the spirit of Stock and Watson (1989), Swanson (1998) and Amato and Swanson (2001).

Original languageEnglish (US)
Title of host publicationForecasting in the Presence of Structural Breaks and Model Uncertainty
PublisherEmerald Group Publishing Ltd.
Pages195-230
Number of pages36
ISBN (Print)9780444529428
DOIs
StatePublished - 2008

Publication series

NameFrontiers of Economics and Globalization
Volume3
ISSN (Print)1574-8715

All Science Journal Classification (ASJC) codes

  • General Economics, Econometrics and Finance

Keywords

  • Block bootstrap
  • Forecasting
  • Model misspecification
  • Nonlinear causality
  • Parameter estimation error
  • Prediction
  • Recursive estimation scheme
  • Rolling estimation scheme

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