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用于模型检验的高斯过程方法AI 解读

This paper proposes a Gaussian process (GP) approach for testing conditional moment restrictions. Tests are based on squared Neyman orthogonal function-parametric processes integrated with respect to a GP distribution. This methodology leads to a general unified framework of kernel-based tests having the following properties: (i) bootstrap tests are easy to implement in the presence of nuisance parameters (they are simple quadratic forms, and there is no need to reestimate…

多值处理下的回归不连续设计AI 解读

Summary We study identification and estimation in the regression discontinuity design with a multivalued treatment. We show that heterogeneity in the first stage discontinuities can be used for the identification of the marginal treatment effects under an alternative assumption, namely, the homogeneity of the LATEs along some covariates. This assumption can often be tested and relaxed. Our estimator can be programmed as a simple two‐stage least squares regression, and…

含非参数不可观测异质性的结构模型之非正则识别AI 解读

One of the most important empirical findings in microeconometrics is the pervasiveness of heterogeneity in economic behavior (cf. Heckman, 2001). This paper shows that cumulative distribution functions and quantiles of the nonparametric unobserved heterogeneity have an infinite efficiency bound in many structural economic models of interest. The paper presents general and precise conditions to prove such results. The usefulness of the theory is demonstrated with several…

局部稳健的半参数估计AI 解读

Many economic and causal parameters depend on nonparametric or high dimensional first steps. We give a general construction of locally robust/orthogonal moment functions for GMM, where first steps have no effect, locally, on average moment functions. Using these orthogonal moments reduces model selection and regularization bias, as is important in many applications, especially for machine learning first steps. Also, associated standard errors are robust to misspecification…

欧拉方程的非参数识别与估计AI 解读

We consider nonparametric identification and estimation of pricing kernels, or equivalently of marginal utility functions up to scale, in consumption-based asset pricing Euler equations. Ours is the first paper to prove nonparametric identification of Euler equations under low level conditions (without imposing functional restrictions or just assuming completeness). We also propose a novel nonparametric estimator based on our identification analysis, which combines standard…

用单一工具变量识别多个边际效应AI 解读

This paper proposes a new strategy for the identification of the marginal effects of an endogenous multivalued variable (which can be continuous, or a vector) in a model with an Instrumental Variable (IV) of lower dimension, which may even be a single binary variable, and multiple controls. Despite the failure of the classical order condition, we show that identification may be achieved by exploiting heterogeneity of the “first stage” in the controls through a new rank…

半参数两步模型的识别与估计AI 解读

Let H0(X) be a function that can be nonparametrically estimated. Suppose E [Y|X]=F0[X⊤β0, H0(X)]. Many models fit this framework, including latent index models with an endogenous regressor and nonlinear models with sample selection. We show that the vector β0 and unknown function F0 are generally point identified without exclusion restrictions or instruments, in contrast to the usual assumption that identification without instruments requires fully specified functional…

用于估计与检验的非参数及半参数残差加权和的一致收敛AI 解读

A new uniform expansion is introduced for sums of weighted kernel-based regression residuals from nonparametric or semiparametric models. This expansion is useful for deriving asymptotic properties of semiparametric estimators and test statistics with data-dependent bandwidths, random trimming, and estimated efficiency weights. Provided examples include a new estimator for a binary choice model with selection and an associated directional test for specification of this…

条件随机占优检验AI 解读

This article proposes bootstrap-based stochastic dominance tests for nonparametric conditional distributions and their moments. We exploit the fact that a conditional distribution dominates the other if and only if the difference between the marginal joint distributions is monotonic in the explanatory variable at each value of the dependent variable. The proposed test statistic compares restricted and unrestricted estimators of the difference between the joint distributions,…

随机单调性的分布无关检验AI 解读

This article proposes a nonparametric test of monotonicity for conditional distributions and its moments. Unlike previous proposals, our method does not require smooth estimation of the derivatives of nonparametric curves. Distinguishing features of our approach are that critical values are pivotal under the null in finite samples and that the test is invariant to any monotonic continuous transformation of the explanatory variable. The test statistic is the sup-norm of the…

聚焦平均导数的单指标限制检验AI 解读

This paper considers a situation where the violation of a single-index restriction is a concern only to the extent that it causes bias to the estimates of the average derivatives. We propose a method to construct tests that concentrate their asymptotic powers upon only such interesting alternatives. The test has a limiting distribution under the null hypothesis, and even accommodates the case where the parameter estimates have a convergence rate slower than as in the case of…