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非经典测量误差模型的工具变量处理AI 解读

While the literature on nonclassical measurement error traditionally relies on the availability of an auxiliary data set containing correctly measured observations, we establish that the availability of instruments enables the identification of a large class of nonclassical nonlinear errors-in-variables models with continuously distributed variables. Our main identifying assumption is that, conditional on the value of the true regressors, some “measure of location” of the…

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弱工具变量回归中的秩检验AI 解读

This paper considers tests in an instrumental variable (IVs) regression model with IVs that may be weak. Tests that have near-optimal asymptotic power properties with Gaussian errors for weak and strong IVs have been determined in Andrews, Moreira, and Stock (2006, Econometrica 74, 715–752). In this paper, we seek tests that have near-optimal asymptotic power with Gaussian errors and improved power with non-Gaussian errors relative to existing tests. Tests with such…

拉丁美洲基础设施特许经营：政府主导的重新谈判AI 解读

Abstract This paper analyzes government‐led renegotiations in infrastructure concession contracts in Latin America, based on the same sample used in Guasch, Laffont and Straub (2003) to examine firm‐led renegotiations. After extending the theoretical framework to a multiple‐period context in which both Pareto‐improving and rent‐shifting renegotiations at the initiative of the government can occur, we develop an original instrumental variable strategy to address the issue of…

使用近似工具变量的测量误差建模AI 解读

Summary Consider using regression modelling to relate an exposure (predictor) variable to a disease outcome (response) variable. If the exposure variable is measured with error, but this error is ignored in the analysis, then misleading inferences can result. This problem is well known and has spawned a large literature on methods which adjust for measurement error in predictor variables. One theme is that the requisite assumptions about the nature of the measurement error…

IV 与 JIVE 估计量的矩AI 解读

We develop a method based on the use of polar coordinates to investigate the existence of moments for instrumental variables and related estimators in the linear regression model. For generalized IV estimators, we obtain familiar results. For JIVE, we obtain the new result that this estimator has no moments at all. Simulation results illustrate the consequences of its lack of moments. Copyright Royal Economic Society 2007

形状不变恩格尔曲线的半非参数工具变量估计AI 解读

This paper studies a shape-invariant Engel curve system with endogenous total expenditure, in which the shape-invariant specification involves a common shift parameter for each demographic group in a pooled system of nonparametric Engel curves. We focus on the identification and estimation of both the nonparametric shapes of the Engel curves and the parametric specification of the demographic scaling parameters. The identification condition relates to the bounded…

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外生性的非参数检验AI 解读

This paper presents a test for exogeneity of explanatory variables that minimizes the need for auxiliary assumptions that are not required by the definition of exogeneity. It concerns inference about a non-parametric function g that is identified by a conditional moment restriction involving instrumental variables (IV). A test of the hypothesis that g is the mean of a random variable Y conditional on a covariate X is developed that is not subject to the ill-posed inverse…

具有过度识别约束的工具变量回归敏感性分析AI 解读

Instrumental variables regression (IV regression) is a method for making causal inferences about the effect of a treatment based on an observational study in which there are unmeasured confounding variables. The method requires one or more valid instrumental variables (IVs); a valid IV is a variable that is associated with the treatment, is independent of unmeasured confounding variables, and has no direct effect on the outcome. Often there is uncertainty about the validity…

含测量误差的受限因变量模型的两阶段估计AI 解读

This paper deals with censored or truncated regression models where the explanatory variables are measured with additive errors. We propose a two-stage estimation procedure that combines the instrumental variable method and the minimum distance estimation. This approach produces consistent and asymptotically normally distributed estimators for model parameters. When the predictor and instrumental variables are normally distributed, we also propose a maximum likelihood based…

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分位数回归模型的非参数工具变量估计AI 解读

We consider nonparametric estimation of a regression function that is identified by requiring a specified quantile of the regression “error” conditional on an instrumental variable to be zero. The resulting estimating equation is a nonlinear integral equation of the first kind, which generates an ill-posed inverse problem. The integral operator and distribution of the instrumental variable are unknown and must be estimated nonparametrically. We show that the estimator is…

分组数据的改进测量误差估计量AI 解读

Grouping models are widely used in economics but are subject to finite sample bias. I show that the standard errors-in-variables estimator is exactly equivalent to the jackknife instrumental variables estimator and use this relationship to develop an estimator which, unlike the standard errors-in-variables estimator, is unbiased in finite samples. The theoretical results are demonstrated using Monte Carlo experiments. Finally, I implement a model of intertemporal male labor…