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若干具有内生性的随机前沿模型AI 解读

We consider mostly Bayesian estimation of stochastic frontier models where one-sided inefficiencies and/or the idiosyncratic error term are correlated with the regressors. We begin with a model where a Chamberlain–Mundlak device is used to relate a transformation of time-invariant effects to the regressors. This basic model is then extended in two directions: first an extra one-sided error term is added to allow for time-varying efficiencies. Second, a model with an equation…

利用分组数据对收入分布进行推断AI 解读

We develop a general approach to estimation and inference for income distributions using grouped or aggregate data that are typically available in the form of population shares and class mean incomes, with unknown group bounds. We derive generic moment conditions and an optimal weight matrix that can be used for generalized method-of-moments (GMM) estimation of any parametric income distribution. Our derivation of the weight matrix and its inverse allows us to express the…

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Probit 模型估计量的小样本性质AI 解读

When maximum likelihood estimates of the coefficients in a nonlinear model such as the probit model are obtained there are a number of asymptotically equivalent covariance matrix estimators that can be used. These covariance matrix estimators are typically associated with different computer algorithms. For example, with the Newton–Raphson algorithm the inverse of the negative of the Hessian matrix from the log-likelihood function is used; with the method of scoring the…

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利用时间序列与横截面数据估计具有正负边际风险的生产函数AI 解读

Two production-function models with error components for time and firms and a heteroscedastic disturbance are proposed. Unlike previous models, these two permit the variance of output to increase or decrease as one of the inputs is increased. When the models and some suggested estimators are applied to the pastoral zone of eastern Australia, the results indicate that labor, water, and possibly fencing, are likely to reduce the variance of wool production; and that sheep, and…

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