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Full-Text Articles in Economics
Sensitivity Analysis Using Approximate Moment Condition Models, Timothy B. Armstrong, Michal Kolesár
Sensitivity Analysis Using Approximate Moment Condition Models, Timothy B. Armstrong, Michal Kolesár
Cowles Foundation Discussion Papers
We consider inference in models defined by approximate moment conditions. We show that near-optimal confidence intervals (CIs) can be formed by taking a generalized method of moments (GMM) estimator, and adding and subtracting the standard error times a critical value that takes into account the potential bias from misspecification of the moment conditions. In order to optimize performance under potential misspecification, the weighting matrix for this GMM estimator takes into account this potential bias, and therefore differs from the one that is optimal under correct specification. To formally show the near-optimality of these CIs, we develop asymptotic efficiency bounds for …
Sensitivity Analysis Using Approximate Moment Condition Models, Timothy B. Armstrong, Michal Kolesár
Sensitivity Analysis Using Approximate Moment Condition Models, Timothy B. Armstrong, Michal Kolesár
Cowles Foundation Discussion Papers
We consider inference in models defined by approximate moment conditions. We show that near-optimal confidence intervals (CIs) can be formed by taking a generalized method of moments (GMM) estimator, and adding and subtracting the standard error times a critical value that takes into account the potential bias from misspecification of the moment conditions. In order to optimize performance under potential misspecification, the weighting matrix for this GMM estimator takes into account this potential bias, and therefore differs from the one that is optimal under correct specification. To formally show the near-optimality of these CIs, we develop asymptotic efficiency bounds for …
Breakdown Point Theory For Implied Probability Bootstrap, Lorenzo Camponovo, Taisuke Otsu
Breakdown Point Theory For Implied Probability Bootstrap, Lorenzo Camponovo, Taisuke Otsu
Cowles Foundation Discussion Papers
This paper studies robustness of bootstrap inference methods under moment conditions. In particular, we compare the uniform weight and implied probability bootstraps by analyzing behaviors of the bootstrap quantiles when outliers take arbitrarily large values, and derive the breakdown points for those bootstrap quantiles. The breakdown point properties characterize the situation where the implied probability bootstrap is more robust than the uniform weight bootstrap against outliers. Simulation studies illustrate our theoretical findings.
X-Differencing And Dynamic Panel Model Estimation, Chirok Han, Peter C.B. Phillips, Donggyu Sul
X-Differencing And Dynamic Panel Model Estimation, Chirok Han, Peter C.B. Phillips, Donggyu Sul
Cowles Foundation Discussion Papers
This paper introduces a new estimation method for dynamic panel models with fixed effects and AR( p ) idiosyncratic errors. The proposed estimator uses a novel form of systematic differencing, called X -differencing, that eliminates fixed effects and retains information and signal strength in cases where there is a root at or near unity. The resulting “panel fully aggregated” estimator (PFAE) is obtained by pooled least squares on the system of X -differenced equations. The method is simple to implement, free from bias for all parameter values, including unit root cases, and has strong asymptotic and finite sample performance characteristics …
Semiparametric Efficiency In Gmm Models Of Nonclassical Measurement Errors, Missing Data And Treatment Effects, Xiaohong Chen, Han Hong, Alessandro Tarozzi
Semiparametric Efficiency In Gmm Models Of Nonclassical Measurement Errors, Missing Data And Treatment Effects, Xiaohong Chen, Han Hong, Alessandro Tarozzi
Cowles Foundation Discussion Papers
We study semiparametric efficiency bounds and efficient estimation of parameters defined through general nonlinear, possibly non-smooth and over-identified moment restrictions, where the sampling information consists of a primary sample and an auxiliary sample. The variables of interest in the moment conditions are not directly observable in the primary data set, but the primary data set contains proxy variables which are correlated with the variables of interest. The auxiliary data set contains information about the conditional distribution of the variables of interest given the proxy variables. Identification is achieved by the assumption that this conditional distribution is the same in both …
Gmm With Many Moment Conditions, Chirok Han, Peter C.B. Phillips
Gmm With Many Moment Conditions, Chirok Han, Peter C.B. Phillips
Cowles Foundation Discussion Papers
This paper provides a first order asymptotic theory for generalized method of moments (GMM) estimators when the number of moment conditions is allowed to increase with the sample size and the moment conditions may be weak. Examples in which these asymptotics are relevant include instrumental variable (IV) estimation with many (possibly weak or uninformed) instruments and some panel data models covering moderate time spans and with correspondingly large numbers of instruments. Under certain regularity conditions, the GMM estimators are shown to converge in probability but not necessarily to the true parameter, and conditions for consistent GMM estimation are given. A …
Fully Modified Iv, Give And Gmm Estimation With Possibly Non-Stationary Regressions And Instruments, Yuichi Kitamura, Peter C.B. Phillips
Fully Modified Iv, Give And Gmm Estimation With Possibly Non-Stationary Regressions And Instruments, Yuichi Kitamura, Peter C.B. Phillips
Cowles Foundation Discussion Papers
This paper develops a general theory of instrumental variables (IV) estimation that allows for both I(1) and I(0) regressors and instruments. The estimation techniques involve an extension of the fully modified (FM) regression procedure that was introduced in earlier work by Phillips-Hansen (1990). FM versions of the generalized instrumental variable estimation (GIVE) method and the generalized method of moments (GMM) estimator are developed. In models with both stationary and nonstationary components, the FM-GIVE and FM-GMM techniques provide efficiency gains over FM-IV in the estimation of the stationary components of a model that has both stationary and nonstationary regressors. The paper …