The Experts below are selected from a list of 234 Experts worldwide ranked by ideXlab platform
Cesare Robotti - One of the best experts on this subject based on the ideXlab platform.
-
further results on the limiting distribution of gmm Sample Moment conditions
Journal of Business & Economic Statistics, 2012Co-Authors: Nikolay Gospodinov, Raymond Kan, Cesare RobottiAbstract:In this article, we examine the limiting behavior of generalized method of Moments (GMM) Sample Moment conditions and point out an important discontinuity that arises in their asymptotic distribution. We show that the part of the scaled Sample Moment conditions that gives rise to degeneracy in the asymptotic normal distribution is T-consistent and has a nonstandard limiting distribution. We derive the appropriate asymptotic (weighted chi-squared) distribution when this degeneracy occurs and show how to conduct asymptotically valid statistical inference. We also propose a new rank test that provides guidance on which (standard or nonstandard) asymptotic framework should be used for inference. The finite-Sample properties of the proposed asymptotic approximation are demonstrated using simulated data from some popular asset pricing models.
-
further results on the limiting distribution of gmm Sample Moment conditions
2012Co-Authors: Nikolay Gospodinov, Raymond Kan, Cesare RobottiAbstract:In this paper, we extend the results in Hansen (1982) regarding the asymptotic distribution of GMM Sample Moment conditions. In particular, we show that the part of the scaled Sample Moment conditions that gives rise to degeneracy in the asymptotic normal distribution is T-consistent and has a non-standard limiting distribution. We derive the asymptotic distribution for a given linear combination of the Sample Moment conditions and show how to conduct statistical inference. We demonstrate the finite-Sample properties of the proposed asymptotic approximation using simulation.
-
further results on the limiting distribution of gmm Sample Moment conditions
2010Co-Authors: Nikolay Gospodinov, Raymond Kan, Cesare RobottiAbstract:In this paper, we extend the results in Hansen (1982) regarding the asymptotic distribution of generalized method of Moments (GMM) Sample Moment conditions. In particular, we show that the part of the scaled Sample Moment conditions that gives rise to degeneracy in the asymptotic normal distribution is T-consistent and has a nonstandard limiting distribution. We derive the asymptotic distribution for a given linear combination of the Sample Moment conditions and show how to conduct statistical inference. We demonstrate the finite-Sample properties of the proposed asymptotic approximation using simulation.
Nikolay Gospodinov - One of the best experts on this subject based on the ideXlab platform.
-
further results on the limiting distribution of gmm Sample Moment conditions
Journal of Business & Economic Statistics, 2012Co-Authors: Nikolay Gospodinov, Raymond Kan, Cesare RobottiAbstract:In this article, we examine the limiting behavior of generalized method of Moments (GMM) Sample Moment conditions and point out an important discontinuity that arises in their asymptotic distribution. We show that the part of the scaled Sample Moment conditions that gives rise to degeneracy in the asymptotic normal distribution is T-consistent and has a nonstandard limiting distribution. We derive the appropriate asymptotic (weighted chi-squared) distribution when this degeneracy occurs and show how to conduct asymptotically valid statistical inference. We also propose a new rank test that provides guidance on which (standard or nonstandard) asymptotic framework should be used for inference. The finite-Sample properties of the proposed asymptotic approximation are demonstrated using simulated data from some popular asset pricing models.
-
further results on the limiting distribution of gmm Sample Moment conditions
2012Co-Authors: Nikolay Gospodinov, Raymond Kan, Cesare RobottiAbstract:In this paper, we extend the results in Hansen (1982) regarding the asymptotic distribution of GMM Sample Moment conditions. In particular, we show that the part of the scaled Sample Moment conditions that gives rise to degeneracy in the asymptotic normal distribution is T-consistent and has a non-standard limiting distribution. We derive the asymptotic distribution for a given linear combination of the Sample Moment conditions and show how to conduct statistical inference. We demonstrate the finite-Sample properties of the proposed asymptotic approximation using simulation.
-
further results on the limiting distribution of gmm Sample Moment conditions
2010Co-Authors: Nikolay Gospodinov, Raymond Kan, Cesare RobottiAbstract:In this paper, we extend the results in Hansen (1982) regarding the asymptotic distribution of generalized method of Moments (GMM) Sample Moment conditions. In particular, we show that the part of the scaled Sample Moment conditions that gives rise to degeneracy in the asymptotic normal distribution is T-consistent and has a nonstandard limiting distribution. We derive the asymptotic distribution for a given linear combination of the Sample Moment conditions and show how to conduct statistical inference. We demonstrate the finite-Sample properties of the proposed asymptotic approximation using simulation.
Yoshimasa Uematsu - One of the best experts on this subject based on the ideXlab platform.
-
Asymptotic Efficiency of the OLS Estimator with Singular Limiting Sample Moment Matrices
Statistics & Probability Letters, 2016Co-Authors: Yoshimasa UematsuAbstract:In the literature on time series analysis, Grenander and Rosenblatt’s theorem is necessary to judge the efficiency of OLS estimators with the requirement of Grenander’s conditions. However, without the conditions, it is not obvious whether the estimator is efficient. In this study, a model with an asymptotically efficient OLS estimator is presented, regardless whether one of the conditions is not satisfied. The regression model is specified with polynomial regressors of a slowly varying function and general stationary disturbances. The regressors are known to display asymptotic singularity in the Sample Moment matrices, and thereby, Grenander’s condition fails.
-
Asymptotic Efficiency of the OLS Estimator with Singular Limiting Sample Moment Matrices
Research Papers in Economics, 2011Co-Authors: Yoshimasa UematsuAbstract:This paper presents a time series model that has an asymptotically efficient ordinary least squares (OLS) estimator, irrespective of the singularity of its limiting Sample Moment matrices. In the literature on stationary time series analysis, Grenander and Rosenblatt's (1957) (G-R) classical result is used to judge the asymptotic efficiency of regression coefficients on deterministic regressors satisfying Grenander's condition. Without this condition, however, it is not obvious that the model is efficient. In this paper, we introduce such a model by proving the efficiency of the model with a slowly varying (SV) regressor under the same condition on error terms constrained in G-R. This kind of regressor is known to display asymptotic singularity in the Sample Moment matrices, as in Phillips (2007), such that Grenander's condition fails.
Wolfgang Gawalek - One of the best experts on this subject based on the ideXlab platform.
-
Magnetic Moment of welded HTS Samples: dependence on the current flowing through the welds
Superconductor Science and Technology, 2002Co-Authors: A. B. Surzhenko, Matthias Zeisberger, Tobias Habisreuther, Doris Litzkendorf, Wolfgang GawalekAbstract:We present a method to calculate the magnetic Moments of the high-temperature superconducting (HTS) Samples which consist of a few welded HTS parts. The approach is generalized for the Samples of various geometrical shapes and an arbitrary number of welds. The obtained relations between the Sample Moment and the density of critical current, which flows through the welds, allow to use the magnetization loops for a quantitative characterization of the weld quality in a wide range of temperatures and/or magnetic fields.
Gregor W. Smith - One of the best experts on this subject based on the ideXlab platform.
-
Calibration as Testing: Inference in Simulated Macroeconomic Models
Journal of Business & Economic Statistics, 1991Co-Authors: Allan W. Gregory, Gregor W. SmithAbstract:A stochastic macroeconomic model with no free parameters can be tested by comparing its features, such as Moments, with those of data. Repeated simulation allows exact tests and gives the distribution of the Sample Moment under the null hypothesis that the model is true. We calculate the size of tests of the model studied by Mehra and Prescott. The approximate size of their test (which seeks to match model-generated, mean, risk-free interest rates and equity premia with historical values) is 0 although alternate, empirical representations of this model economy or alternate Moment-matching tests yield large probabilities of Type I error.
