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识别失败与模型设定正确性的协调检验AI 解读

We develop a specification test for GMM and Minimum Distance (MD) inference that is: (i) robust to weak identification; (ii) easy to implement and broadly applicable; and (iii) has meaningful power. Our specification test is based on a conditional inference approach: information about the existence of strongly identified directions in the parameter space is exploited to define a data-dependent critical value, which, in turn, increases the power of the resulting specification…

利用大量辅助统计量进行模拟估计与长期动态分析AI 解读

The existing asymptotic theory for estimators obtained by simulated minimum distance does not cover situations in which the number of components of the auxiliary statistics (or number of matched “moments”) is large — typically larger than the sample size. We establish the consistency of the simulated minimum distance estimator in this situation and derive its asymptotic distribution. Our estimator is easy to implement and allows us to exploit all the informational content of…

线性工具变量模型中对识别强度稳健的非参数推断AI 解读

For a linear IV regression, we propose two new inference procedures on parameters of endogenous variables that are robust to any identification pattern, do not rely on a linear first-stage equation, and account for heteroskedasticity of unknown form. Building on Bierens (1982), we first propose an Integrated Conditional Moment (ICM) type statistic constructed by setting the parameters to the value under the null hypothesis. The ICM procedure tests at the same time the value…

基于模拟伪匹配的识别稳健推断AI 解读

We develop a general simulation-based inference procedure for partially specified models. Our procedure is based on matching auxiliary statistics to simulated counterparts where nuisance parameters are calibrated neither assuming identification of parameters of interest nor a one-to-one binding function. The conditions underlying the asymptotic validity of our (pseudo-)simulators in conjunction with appropriate bootstraps are characterized beyond the strict and exact…

Summary In a partially linear conditional moment model we propose a new estimator for the slope parameter of the endogenous variable of interest, which combines a Robinson’s transformation to partial out the nonlinear part of the model, with a smooth minimum distance approach to exploit all the information of the conditional mean independence restriction. Our estimator only depends on one tuning parameter, is easy to compute, consistent and $\sqrt{n}$-asymptotically normal…

基于指数倾斜 Hellinger 距离的稳健估计AI 解读

This paper is concerned with estimation of parameters defined by moment equalities. We introduce the exponentially tilted Hellinger distance (ETHD) estimator which is efficient under correct specification, and robust to both global and local misspecification. In the spirit of Schennach (2007), ETHD combines the Hellinger distance and the Kullback–Leibler information criterion. We show that it achieves optimal minimax robust properties under local deviations from the model…

识别强度检验AI 解读

We consider models defined by a set of moment restrictions that may be subject to weak identification. We propose a testing procedure to assess whether instruments are “too weak” for standard (Gaussian) asymptotic theory to be reliable. Since the validity of standard asymptotics for GMM rests upon a Taylor expansion of the first order conditions, we distinguish two cases: (i) models that are either linear or separable in the parameters of interest; (ii) general models that…

时变信息下的有效估计与新凯恩斯主义菲利普斯曲线AI 解读

Empirical evidence suggests that many macroeconometric and financial models are subject to both instability and identification problems. We address both issues under the unified framework of time-varying information, which includes changes in instrument strength, in second moment of instruments, and in the variance of moment conditions. Our new estimation method exploits these changes for increased efficiency of the estimates of the (stable) structural parameters. We also…

半强识别下的条件矩模型AI 解读

We consider conditional moment models under semi-strong identification. Identification strength is directly defined through the conditional moments that flatten as the sample size increases. Our new minimum distance estimator is consistent, asymptotically normal, robust to semi-strong identification, and does not rely on the choice of a user-chosen parameter, such as the number of instruments or some smoothing parameter. Heteroskedasticity-robust inference is possible…

多重收敛速度下的有效最小距离估计AI 解读

This paper extends the asymptotic theory of GMM inference to allow sample counterparts of the estimating equations to converge at (multiple) rates, different from the usual square-root of the sample size. In this setting, we provide consistent estimation of the structural parameters. In addition, we define a convenient rotation in the parameter space (or reparametrization) to disentangle the different rates of convergence. More precisely, we identify special linear…

This paper is in the line of the recent literature on weak instruments, which, following the seminal approach of Stock and Wright captures weak identification by drifting population moment conditions. In contrast with most of the existing literature, we do not specify a priori which parameters are strongly or weakly identified. We rather consider that weakness should be related specifically to instruments, or more generally to the moment conditions. In addition, we focus…

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