Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/286579 
Erscheinungsjahr: 
2023
Quellenangabe: 
[Journal:] SERIEs - Journal of the Spanish Economic Association [ISSN:] 1869-4195 [Volume:] 14 [Issue:] 3/4 [Year:] 2023 [Pages:] 253-300
Verlag: 
Springer, Heidelberg
Zusammenfassung: 
Arellano (J Econ 42:247-265, 1989a) showed that valid equality restrictions on covariance matrices could result in efficiency losses for Gaussian PMLEs in simultaneous equations models. We revisit his two-equation example using finite normal mixtures PMLEs instead, which are also consistent for mean and variance parameters regardless of the true distribution of the shocks. Because such mixtures provide good approximations to many distributions, we relate the asymptotic variance of our estimators to the relevant semiparametric efficiency bound. Our Monte Carlo results indicate that they systematically dominate MD and that the version that imposes the valid covariance restriction is more efficient than the unrestricted one.
Schlagwörter: 
Covariance restrictions
Distributional misspecification
Efficiencybound
Finite normal mixtures
Partial adaptivity
Sieves
JEL: 
C30
C36
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