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非光滑准则函数下的间接推断AI 解读

Indirect inference requires simulating realizations of endogenous variables from the model under study. When the endogenous variables are discontinuous functions of the model parameters, the resulting indirect inference criterion function is discontinuous and does not permit the use of derivative-based optimization routines. Using a change of variables technique, we propose a novel simulation algorithm that alleviates the discontinuities inherent in such indirect inference…

依赖结构限制下仅有两期数据的双重差分分位数处理效应AI 解读

This paper shows that the Conditional Quantile Treatment Effect on the Treated is identified under (i) a Conditional Distributional Difference in Differences assumption and (ii) a new assumption that the dependence (the copula) between the change in untreated potential outcomes and the initial level of untreated potential outcomes is the same for the treated group and untreated group. We consider estimation and inference with discrete covariates and propose a uniform…

短面板固定效应删失分位数回归模型的集合识别AI 解读

This paper studies identification and estimation of a censored quantile regression model for short panel data with fixed effects. Using the redistribution-of-mass idea, we obtain bounds on the conditional distribution of differences of the model across periods, under conditional quantile restrictions together with a weak conditional independence assumption along the lines of Rosen (2012). The inversion of the distribution bounds characterizes the sharp identified set via a…

美国离婚法改革与离婚率：交互固定效应方法AI 解读

SUMMARY This paper estimates the effects of unilateral divorce laws on divorce rates in the USA from a panel of state‐level divorce rates. We use the interactive fixed‐effects model to address the issue of endogeneity due to the association between cross‐state unobserved heterogeneity and divorce law reforms. We document that earlier studies in the literature do not fully control for unobserved heterogeneity and result in mixed empirical evidence on the effects of divorce…

回归分位数中结构变化的估计AI 解读

This paper considers the estimation of multiple structural changes occurring at unknown dates in one or multiple conditional quantile functions. The analysis covers time series models as well as models with repeated cross-sections. We estimate the break dates and other parameters jointly by minimizing the check function over all permissible break dates. The limiting distribution of the estimator is derived and the coverage property of the resulting confidence interval is…