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无排除变量的半参数样本选择模型点识别AI 解读

Sample selection is pervasive in applied economic studies. This paper proposes semiparametric selection models that achieve point identification without relying on exclusion restrictions. Our identification conditions require at least one continuously distributed covariate and certain nonlinearity in the selection process. We propose a two-step sieve plug-in estimator that is $\sqrt{n}$-consistent, asymptotically normal, and computationally straightforward, allowing for…

多维匹配的半非参数模型：最优运输方法AI 解读

This paper develops a set of empirically tractable and flexible sieve estimators for semi-nonparametric multidimensional matching models with transferable utility, focusing on worker-job matching. We generalize the parametric quadratic-Gaussian framework employed by Bojilov and Galichon (2016) and Lindenlaub (2017), which relies on joint normality of observed characteristics. We allow unrestricted distributions of characteristics and show identification of the production…

连续时间马尔可夫模型矩与密度的闭式近似AI 解读

This paper develops power series expansions of a general class of moment functions, including transition densities and option prices, of continuous-time Markov processes, including jump--diffusions. The proposed expansions extend the ones in Kristensen and Mele (2011) to cover general Markov processes. We demonstrate that the class of expansions nests the transition density and option price expansions developed in Yang, Chen, and Wan (2019) and Wan and Yang (2021) as special…