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Asymptotic Variance of Test Statistics in ML and QML Frameworks
Bera, Anil K. - 2020
In this study, we consider the test statistics that can be written as the sample average of data and derive their limiting distribution under the maximum likelihood (ML) and the quasi-maximum likelihood (QML) frameworks. We first generalize the asymptotic variance formula suggested in Pierce...
Persistent link: https://www.econbiz.de/10012853408
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Simple Tests for Endogeneity of Spatial Weights Matrices
Bera, Anil K. - 2018
In this study, we propose a Rao's score (RS) statistic (Lagrange multiplier (LM) statistic) to test for endogeneity of the spatial weights matrix in a spatial autoregressive model. To achieve this, we start with a spatial autoregressive model with an acceptable form for the generating process...
Persistent link: https://www.econbiz.de/10012931985
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Asymptotic Distribution of Test Statistics : The Estimating Equation Approach and the Delta Method
Bera, Anil K. - 2018
The delta method that consists of a Taylor approximation can be used to determine the asymptotic variance and distribution of test statistics. In an alternative approach, the test statistic can be combined with some estimating equations in the M-estimation framework for the purpose of deriving...
Persistent link: https://www.econbiz.de/10012931987
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Heteroskedasticity Consistent Covariance Matrix Estimators for Spatial Autoregressive Models
Taspinar, Suleyman - 2018
In the presence of heteroskedasticity, conventional test statistics based on the ordinary least square estimator lead to incorrect inference results for the linear regression model. Given that heteroskedasticity is common in cross-sectional data, the test statistics based on various forms of...
Persistent link: https://www.econbiz.de/10012931988
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Adjustments of Rao's Score Test for Distributional and Local Parametric Misspecifications
Bera, Anil K. - 2019
Rao's (1948) seminal paper introduced a fundamental principle of testing based on the score function and the score test has local optimal properties. When the assumed model is misspecified, it is well known that Rao's score (RS) test loses its optimality. A model could be misspecified in a...
Persistent link: https://www.econbiz.de/10012900591
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Testing Spatial Dependence in Spatial Models with Endogenous Weights Matrices
Bera, Anil K. - 2018
In this study, we propose simple test statistics for identifying the source of spatial dependence in spatial autoregressive models with endogenous weights matrices. Elements of the weights matrices are modelled in such a way that endogenity arises when the unobserved factors that affect elements...
Persistent link: https://www.econbiz.de/10012920801
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Robust LM Tests for Spatial Dynamic Panel Data Models
Bera, Anil K. - 2018
likelihood estimator for the estimation of SDPD models can have asymptotic bias because of individual and time fixed effects …. Bias arises since the limiting distribution of the score functions derived from the corresponding concentrated log … bias on the standard LM test statistics. Second, we further adjust score functions such that the resulting LM test …
Persistent link: https://www.econbiz.de/10012931986
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Bayesian Inference in Spatial Stochastic Volatility Models : An Application to House Price Returns in Chicago
Taspinar, Suleyman - 2019
In this study, we propose a spatial stochastic volatility model in which the latent log-volatility terms follow a spatial autoregressive process. Though there is no spatial correlation in the outcome equation (the mean equation), the spatial autoregressive process defined for the log-volatility...
Persistent link: https://www.econbiz.de/10012900218
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Specification Tests for Spatial Panel Data Models
Bera, Anil K. - 2020
Specification of a model is one of the most fundamental problems in econometrics. In practice, specification tests are generally carried out in a piecemeal fashion, for example, testing the presence of one-effect at a time ignoring the potential presence of other forms of misspecification. Many...
Persistent link: https://www.econbiz.de/10012851191
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Nested and non-nested procedures for testing linear and log-linear regression models
Bera, Anil K.; McAleer, Michael - 1988
Persistent link: https://www.econbiz.de/10000124649