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Score Tests for Zero Covariances in Recursive Linear Models for Grouped or Censored Data

Опубликовано на портале: 01-07-2004
Journal of Econometrics. 1985.  Vol. 28. No. 3. P. 291-305. 
Тематический раздел:
Efficient estimation of normal linear simultaneous equations systems is frequently easier when error covariances are zero. The score test is examined for the hypothesis that an error covariance is zero in a 2-equation recursive normal linear simultaneous equations system where endogenous variates may be completely observed, censored, or grouped. The model contains the seemingly unrelated regression equation model and its analogues for grouped and censored data as special cases. The score test for the hypothesis explores the sample covariance of suitably defined residuals and is closely related to the Information Matrix test calculated for the restricted model in which the error covariance is zero. These results are obtained by viewing a non-zero error covariance as emerging from correlated random variation in intercept parameters that can be detected through the use of Chesher's (1984) test for neglected heterogeneity

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