Alternative approaches to testing non—nested models with auto correlated disturbances

Michael McAleer, Anil K. Bera, M. Hashem Pesaran

Research output: Contribution to journalArticlepeer-review


Since departures from the classical assumptions regarding the disturbances in a linear regression model arise frequently in empirical applications, several computationally straightforward procedures are presented in this paper for testing non—nested models when the disturbances of these models follow first— or higher—order autoregressive processes. An empirical example is used to illustrate how the procedures may be used to test competing Keynesian and New Classical non—nested models of unemployment for the U.S. using annual time series data for 1955—85.

Original languageEnglish (US)
Pages (from-to)3619-3644
Number of pages26
JournalCommunications in Statistics - Theory and Methods
Issue number10
StatePublished - 1990
Externally publishedYes


  • alternative models of unemployment
  • serial correlation
  • specifications tests: non-nested models

ASJC Scopus subject areas

  • Statistics and Probability


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