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连续内生变量与离散工具变量下非参数回归的迭代估计AI 解读

We consider a nonparametric regression model with continuous endogenous independent variables when only discrete instruments are available that are independent of the error term. Although this framework is very relevant for applied research, its implementation is challenging, as the regression function becomes the solution to a nonlinear integral equation. We propose a simple iterative procedure to estimate such models and showcase some of its asymptotic properties. In a…

非参数不可分模型中的条件秩估计与外生性检验AI 解读

Consider a nonparametric nonseparable regression model Y = ϕ(Z, U), where ϕ(Z, U) is strictly increasing in U and U ∼ U[0, 1]. We suppose that there exists an instrument W that is independent of U. The observable random variables are Y, Z, and W, all one-dimensional. We construct test statistics for the hypothesis that Z is exogenous, that is, that U is independent of Z. The test statistics are based on the observation that Z is exogenous if and only if V = FY|Z(Y|Z) is…

效率前沿的非参数工具变量估计AI 解读

The paper investigates endogeneity issues in nonparametric frontier models. It considers a nonseparable model for a cost function C=φ(Y,U) where C and Y are the cost and the output, U is uniform in [0,1] and φ is increasing with respect to U. The cost frontier corresponds to U=0 and U can be interpreted as a normalized level of inefficiency. The endogeneity issue arises when Y is dependent of U. For identification and estimation, we use a nonparametric instrumental variables…

含内生变量的面板数据模型之非参数分析AI 解读

This paper considers the estimation of panel data models by first differences in the presence of endogenous variables and under an instrumental variables condition. This framework leads to the resolution of linear inverse problems solved using a Tikhonov regularization with L2 or Sobolev penalty. Rates of convergence and data driven selection of the regularization parameters are proposed. The practical implementation of our estimators is presented and some Monte Carlo…

通过求解逆问题进行非参数估计：以变换模型为例AI 解读

This paper considers a semi‐parametric version of the transformation model φ (Y) =β′X+U under exogeneity or instrumental variables assumptions (E(U∣X) = 0 or . This model is used as an example to illustrate the practice of the estimation by solving linear functional equations. This paper is specially focused on the data‐driven selection of the regularization parameter and of the bandwidths. Simulations experiments illustrate the relevance of this approach.