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Full-Text Articles in Econometrics
Prediction In A Generalized Spatial Panel Data Model With Serial Correlation, Badi H. Baltagi, Long Liu
Prediction In A Generalized Spatial Panel Data Model With Serial Correlation, Badi H. Baltagi, Long Liu
Center for Policy Research
This paper considers the generalized spatial panel data model with serial correlation proposed by Lee and Yu (2012) which encompasses a lot of the spatial panel data models considered in the literature, and derives the best linear unbiased predictor (BLUP) for that model. This in turn provides valuable BLUP for several spatial panel models as special cases.
Testing For Spatial Lag And Spatial Error Dependence In A Fixed Effects Panel Data Model Using Double Length Artificial Regressions, Badi H. Baltagi, Long Liu
Testing For Spatial Lag And Spatial Error Dependence In A Fixed Effects Panel Data Model Using Double Length Artificial Regressions, Badi H. Baltagi, Long Liu
Center for Policy Research
This paper revisits the joint and conditional Lagrange Multiplier tests derived by Debarsy and Ertur (2010) for a fixed effects spatial lag regression model with spatial auto-regressive error, and derives these tests using artificial Double Length Regressions (DLR). These DLR tests and their corresponding LM tests are compared using an empirical example and a Monte Carlo simulation.
Test Of Hypotheses In A Time Trend Panel Data Model With Serially Correlated Error Component Disturbances, Chihwa Kao, Badi H. Baltagi, Long Liu
Test Of Hypotheses In A Time Trend Panel Data Model With Serially Correlated Error Component Disturbances, Chihwa Kao, Badi H. Baltagi, Long Liu
Center for Policy Research
This paper studies test of hypotheses for the slope parameter in a linear time trend panel data model with serially correlated error component disturbances. We propose a test statistic that uses a bias corrected estimator of the serial correlation parameter. The proposed test statistic which is based on the corresponding fixed effects feasible generalized least squares (FE-FGLS) estimator of the slope parameter has the standard normal limiting distribution which is valid whether the remainder error is I(0) or I(1). This performs well in Monte Carlo experiments and is recommended.