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多工具变量下的随机效应估计量AI 解读

In this paper we propose a new estimator for a model with one endogenous regressor and many instrumental variables. Our motivation comes from the recent literature on the poor properties of standard instrumental variables estimators when the instrumental variables are weakly correlated with the endogenous regressor. Our proposed estimator puts a random coefficients structure on the relation between the endogenous regressor and the instruments. The variance of the random…

结构模型的条件似然比检验AI 解读

This paper develops a general method for constructing exactly similar tests based on the conditional distribution of nonpivotal statistics in a simultaneous equations model with normal errors and known reduced-form covariance matrix. These tests are shown to be similar under weak-instrument asymptotics when the reduced-form covariance matrix is estimated and the errors are non-normal. The conditional test based on the likelihood ratio statistic is particularly simple and has…

半参数弱工具变量回归及其风险—收益权衡应用AI 解读

We extend the local-to-zero analysis of models with weak instruments to models with estimated instruments and regressors and with higher-order dependence between instruments and disturbances. This framework is applicable to linear models with expectation variables that are estimated nonparametrically, such as the risk-return tradeoff in finance and the effect of inflation uncertainty on real economic activity. Our simulation evidence suggests that Lagrange multiplier…

广义矩方法中的弱工具变量与弱识别综述AI 解读

Weak instruments arise when the instruments in linear instrumental variables (IV) regression are weakly correlated with the included endogenous variables. In generalized method of moments (GMM), more generally, weak instruments correspond to weak identification of some or all of the unknown parameters. Weak identification leads to GMM statistics with nonnormal distributions, even in large samples, so that conventional IV or GMM inferences are misleading. Fortunately, various…

可忽略性不确定下的小区域二元数据分层贝叶斯无响应模型AI 解读

In the National Crime Survey (NCS), data on victimization can be poststratified into domains determined by urbanization, type of place, and poverty level. There is much difficulty in the analysis of binary data with substantial nonresponse. We consider three Bayesian hierarchical models for binary nonresponse data, like those from the NCS, which are clustered within a number of domains or areas. As in small area estimation, one key feature is that each model “borrows…

跨期劳动供给模型工具变量估计的有限样本偏误：跨期替代弹性真的很小吗？AI 解读

The empirical literature on intertemporal labor supply behavior documents that the intertemporal elasticity of substitution of labor supply is very low, with a plausible range of zero to 0.2. Drawing upon the literature on the distribution of instrumental variables (IV) estimators, this paper demonstrates that this conventional wisdom is erroneous because it does not take into account the severe finite sample bias in these estimates that arise from weak instruments. This…

弱识别下的广义矩估计AI 解读

This paper develops asymptotic distribution theory for GMM estimators and test statistics when some or all of the parameters are weakly identified. General results are obtained and are specialized to two important cases: linear instrumental variables regression and Euler equations estimation of the CCAPM. Numerical results for the CCAPM demonstrate that weak-identification asymptotics explains the breakdown of conventional GMM procedures documented in previous Monte Carlo…

弱工具变量下LIML与刀切工具变量估计量的小样本性质AI 解读

Using Monte Carlo simulations we study the small sample performance of the traditional TSLS, the LIML and four new jackknife IV estimators when the instruments are weak. We find that the new estimators and LIML have a smaller bias but a larger variance than the TSLS. In terms of root mean square error, neither LIML nor the new estimators perform uniformly better than the TSLS. The main conclusion from the simulations and an empirical application on labour supply functions is…

弱工具变量回归中结构参数的推断AI 解读

The authors consider the problem of making asymptotically valid inference on structural parameters in instrumental variables regression with weak instruments. Using local-to-zero asymptotics, they derive the asymptotic distributions of likelihood ratio (LR) and Lagrange multiplier (LM) type statistics for testing simple hypotheses on structural parameters based on maximum likelihood and generalized methods of moments estimation methods. In contrast to the nonstandard…

计量经济学中的若干不可能性定理及其在结构与动态模型中的应用AI 解读

General characterizations of valid confidence sets and tests in problems involving (locally almost) unidentified (LAU) parameters are presented. In particular, any valid confidence set for an unbounded LAU parameter must be unbounded with positive probability. Consequently, almost surely bounded confidence sets, like Wald-type confidence sets, have zero coverage probability and Wald-type test statistics cannot be pivotal functions. The results are applied to simultaneous…

弱工具变量下的工具变量回归AI 解读

This paper develops asymptotic distribution theory for instrumental variables regression when the partial correlations between the instruments and the endogenous variables are weak, here modeled as local to zero. Asymptotic representation are provided for various statistics, including two-stage least squares and limited information maximum likelihood estimators, Wald statistics, and statistics testing overidentification and endogeneity. The asymptotic distributions provide…