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多方程模型中的半参数贝叶斯推断AI 解读

Abstract This paper outlines an approach to Bayesian semiparametric regression in multiple equation models which can be used to carry out inference in seemingly unrelated regressions or simultaneous equations models with nonparametric components. The approach treats the points on each nonparametric regression line as unknown parameters and uses a prior on the degree of smoothness of each line to ensure valid posterior inference despite the fact that the number of parameters…

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辍学者会因辍学受损吗？广义选择模型中的结果增益估计与预测AI 解读

Abstract In this paper we describe methods for predicting distributions of outcome gains in the framework of a latent variable selection model. We describe such procedures for Student‐ t selection models and a finite mixture of Gaussian selection models. Importantly, our algorithms for fitting these models are simple to implement in practice, and also permit learning to take place about the non‐identified cross‐regime correlation parameter. Using data from High School and…

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求职搜寻模型中的最优性检验AI 解读

Models of search in labor markets are potentially of great use for policy analy-sis since their parameters are structural. However, a common feature of these models is that an assumption of optimal behavior on the part of agents is necessary to achieve identifica-tion. From a classical econometric perspective, this means the assumption of optimality is untestable and, if optimality is not imposed, it is impossible to learn about the unidentified parameters. This paper argues…

非识别模型中的信念更新AI 解读

A Bayesian analysis of a nonidentified model is always possible if a proper prior on all the parameters is specified. There is, however, no Bayesian free lunch. The “price” is that there exist quantities about which the data are uninformative, i.e., their marginal prior and posterior distributions are identical. In the case of improper priors the analysis is problematic—resulting posteriors can be improper. This study investigates both proper and improper cases through a…

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计量经济学的方法论AI 解读

The methodology of econometrics is concerned with rules governing the building of statistical models in economics. These two volumes draw together 62 previously published studies in economics and statistics. The volumes are divided into 12 sections covering controversial topics ranging from the earliest days of econometrics to the present. Sections include policy analysis, exogeneity, causality, Bayesian perspective and British econometrics.

重返论争前线AI 解读

This study updates previous work by the author providing quantitative evidence of the professional impact of the Bayesian viewpoint as measured by the percentage of pages devoted to Bayesian articles in leading statistics and econometrics journals over the period 1982–1991.

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新古典宏观框架下名义货币与实际产出的贝叶斯分析AI 解读

This study investigates the empirical evidence on the effects of unanticipated changes in nominal money on real output in 47 countries when viewed through a window (i.e., likelihood function) that assumes the neutrality of anticipated changes. Using a Bayesian predictivist approach, it provides a pedagogical Bayesian analysis of generated regressor models in the face of specification uncertainty involving, among other things, multiple unit root and trend stationary…

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一份来自前线的报告AI 解读

This study provides quantitative evidence of the professional impact of the Bayesian viewpoint as measured by the percentage of pages devoted to Bayesian topics in econometrics textbooks and leading econometrics and statistics journals.

观测相关情形下的Probit估计AI 解读

Estimation of limited dependent variable models with dependent observations has received relatively little attention due to the computational complexity of the maximum likelihood estimator. We develop a computationally attractive and relatively efficient estimator for this case that utilises the orthogonality conditions. The resulting Generalized Conditional Moment (GCM) estimators can be applied with a known or an unknown disturbance covariance matrix. Although the paper…

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幂指数分布族内的正态性诊断检验AI 解读

This article develops the locally uniformly most powerful unbiased Lagrange multiplier test of normality of regression disturbances within the family of power exponential distributions. The small sample power properties of the test are compared in a Monte Carlo study with 6 well-known tests across 12 alternative nonnormal distributions. In addition, the finite sample power properties for nonnormal alternatives within the power exponential family are summarized by estimating…

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