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《计量经济学中的半参数方法》书评AI 解读

Semiparametric and nonparametric estimation have attracted a great deal of attention from statisticians and theoretical econometricians in the past decade. Various new models have been proposed, and new methods for estimating those models have been suggested. These new models and methods are scattered in various academic journals and are not easily accessible to other researchers. Moreover, the new methods are technical, not easily understood by nonexperts such as graduate…

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随机生产前沿模型的半参数估计AI 解读

This paper extends the linear stochastic frontier model proposed by D. J. Aigner, C. A. K. Lovell, and P. Schmidt (1977) to a semiparametric frontier model in which the functional form of the production frontier is unspecified and the distributions of the composite error terms are of known form. Pseudo-likelihood estimators of the parameters characterizing the two error terms of the model are constructed based on kernel estimation of the conditional mean function. The Monte…

一致模型设定检验：遗漏变量与半参数函数形式AI 解读

By using nonparametric kernel estimation method and a central limit theorem for degenerate U-statistics of order higher than two, the authors develop several consistent model specification tests in the context of a nonparametric regression model. These include tests for omitted variables, tests for a partially linear model, and tests for a semiparametric single index model. The asymptotic normality of the test statistics are established under the respective null hypotheses…

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具有一般 MA(q) 扰动的误差成分模型估计AI 解读

This paper provides a simple estimation method for an error component regression model with general MA( q ) remainder disturbances. The estimation method utilizes the transformation derived by Baltagi and Li [3] for an error component model with autoregressive remainder disturbances, and a standard orthogonalizing algorithm for the general MA( q ) model. This estimation method is computationally simple utilizing only least-squares regressions. This is important for panel…

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含误差分量的联立方程估计札记AI 解读

Baltagi [3] derived 2SLS and 3SLS analogues for a simultaneous equation model with error components. These were denoted by EC2SLS and EC3SLS. More recently, Balestra and Varadharajan-Krishnakumar [1] derived alternative 2SLS and 3SLS analogues; these were denoted by G2SLS and G3SLS. This note explains the relationship between these estimators and shows that the set of instruments employed by Balestra and Varadharajan-Krishnakumar is a subset of those used in Baltagi. In…

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