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Bootstrap Lm Tests For Higher-Order Spatial Effects In Spatial Linear Regression Models, Zhenlin Yang
Bootstrap Lm Tests For Higher-Order Spatial Effects In Spatial Linear Regression Models, Zhenlin Yang
Research Collection School Of Economics
This paper first extends the methodology of Yang (J Econom 185:33-59, 2015) to allow for non-normality and/or unknown heteroskedasticity in obtaining asymptotically refined critical values for the LM-type tests through bootstrap. Bootstrap refinements in critical values require the LM test statistics to be asymptotically pivotal under the null hypothesis, and for this we provide a set of general methods for constructing LM and robust LM tests. We then give detailed treatments for two general higher-order spatial linear regression models: namely the model and the model, by providing a complete set of non-normality robust LM and bootstrap LM tests for higher-order …
Diagnostic Tests For Homoskedasticity In Spatial Cross-Sectional Or Panel Models, Badi H. Baltagi, Alain Pirotte, Zhenlin Yang
Diagnostic Tests For Homoskedasticity In Spatial Cross-Sectional Or Panel Models, Badi H. Baltagi, Alain Pirotte, Zhenlin Yang
Research Collection School Of Economics
We propose tests for homoskedasticity in spatial econometric models, based on joint or concentrated score functions and an Outer-Product-of-Martingale-Difference (OPMD) estimate of the variance of the joint or concentrated score functions. Versions of these tests robust against non-normality are also given. Asymptotic properties of the proposed tests are formally examined using a cross-section model and a panel model with fixed effects. Monte Carlo results show that the proposed tests based on the concentrated score function have good finite sample properties. Finally, the generality of the proposed approach in constructing tests for homoskedasticity is further demonstrated using a spatial dynamic panel …