SUR estimation of multiple time-series models with heteroscedasticity and serial correlation of unknown form

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Abstract

Ordinary least squares (OLS) estimation with non-parametric estimation of the coefficient's covariance matrix is a widely used procedure when the pattern of correlations of the errors is unknown. With multiple time series the seemingly unrelated regressions (SUR) estimator is a natural alternative to OLS. Simulation results show that the SUR estimator can be substantially more efficient than OLS. A non-parametric covariance matrix estimator is still required to deal with remaining heteroscedasticity and serial correlation. Further refinements are possible when there is more specific prior information on the conditional autocovariances.
Original languageEnglish
Pages (from-to)239-245
JournalEconomics Letters
Volume53
DOIs
Publication statusPublished - 1 Dec 1996

Keywords

  • Multiple time-series models
  • Non-parametric covariance matrix estimation

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