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Full-Text Articles in Social and Behavioral Sciences
Improved Inferences For Spatial Regression Models, Shew Fan Liu, Zhenlin Yang
Improved Inferences For Spatial Regression Models, Shew Fan Liu, Zhenlin Yang
Research Collection School Of Economics
The quasi-maximum likelihood (QML) method is popular in the estimation and inference for spatial regression models. However, the QML estimators (QMLEs) of the spatial parameters can be quite biased and hence the standard inferences for the regression coefficients (based on t-ratios) can be seriously affected. This issue, however, has not been addressed. The QMLEs of the spatial parameters can be bias-corrected based on the general method of Yang (2015b, J. of Econometrics 186, 178-200). In this paper, we demonstrate that by simply replacing the QMLEs of the spatial parameters by their bias-corrected versions, the usual t-ratios for the regression coefficients …
A Bayesian Specification Test, Yong Li, Tao Zeng, Jun Yu
A Bayesian Specification Test, Yong Li, Tao Zeng, Jun Yu
Research Collection School Of Economics
A Bayesian test statistic is proposed to assess the model specification after the model is estimated by Bayesian MCMC methods. The proposed approach does not require an alternative model to be specified and is applicable to a variety of models, including latent variable models, structural dynamic choice models, and dynamics stochastic general equilibrium (DSGE) models, for which frequentist methods are difficult to use. The properties of the test statistic are established and its implementation is discussed. The test is easy to use and the test statistic can be calculated from MCMC outputs even when there are latent variables. The method …
Asymptotic Distribution And Finite-Sample Bias Correction Of Qml Estimators For Spatial Dependence Model, Shew Fan Liu, Zhenlin Yang
Asymptotic Distribution And Finite-Sample Bias Correction Of Qml Estimators For Spatial Dependence Model, Shew Fan Liu, Zhenlin Yang
Research Collection School Of Economics
In studying the asymptotic and finite sample properties of quasi-maximum likelihood (QML) estimators for the spatial linear regression models, much attention has been paid to the spatial lag dependence (SLD) model; little has been given to its companion, the spatial error dependence (SED) model. In particular, the effect of spatial dependence on the convergence rate of the QML estimators has not been formally studied, and methods for correcting finite sample bias of the QML estimators have not been given. This paper fills in these gaps. Of the two, bias correction is particularly important to the applications of this model, as …
A General Method For Third-Order Bias And Variance Corrections On A Nonlinear Estimator, Zhenlin Yang
A General Method For Third-Order Bias And Variance Corrections On A Nonlinear Estimator, Zhenlin Yang
Research Collection School Of Economics
Motivated by a recent study of Bao and Ullah (2007a) on finite sample properties of MLE in the pure SAR (spatial autoregressive) model, a general method for third-order bias and variance corrections on a nonlinear estimator is proposed based on stochastic expansion and bootstrap. Working with concentrated estimating equation simplifies greatly the high-order expansions for bias and variance; a simple bootstrap procedure overcomes a major difficulty in analytically evaluating expectations of various quantities in the expansions. The method is then studied in detail using a more general SAR model, with its effectiveness in correcting bias and improving inference fully demonstrated …
Asymptotic Distribution And Finite Sample Bias Correction Of Qml Estimators For Spatial Error Dependence Model, Shew Fan Liu, Zhenlin Yang
Asymptotic Distribution And Finite Sample Bias Correction Of Qml Estimators For Spatial Error Dependence Model, Shew Fan Liu, Zhenlin Yang
Research Collection School Of Economics
In studying the asymptotic and finite sample properties of quasi-maximum likelihood (QML) estimators for the spatial linear regression models, much attention has been paid to the spatial lag dependence (SLD) model; little has been given to its companion, the spatial error dependence (SED) model. In particular, the effect of spatial dependence on the convergence rate of the QML estimators has not been formally studied, and methods for correcting finite sample bias of the QML estimators have not been given. This paper fills in these gaps. Of the two, bias correction is particularly important to the applications of this model, as …