SUR estimation of multiple time-series models with heteroscedasticity and serial correlation of unknown form
M Creel, M Farell - Economics letters, 1996 - Elsevier
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 …
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 …
