Hausman, Autocorrelation Test and Heteroscedasticity, Paragraphs

542 WordsFeb 21, 20182 Pages
Hausman test Hausman test which usually accepted method of selecting between random and fixed effects which is running on regression equation. Hausman (1978) provided a tectonic change in interpretation related to the specification of econometric models. The seminal insight that one could compare two models which were both consistent under the null spawned a test which was both simple and powerful. The so-called ‘Hausman test’ has been applied and extended theoretically in a variety of econometric domains. We focus on the construction of the Hausman test in a variety of panel data settings, and in particular, the recent adaptation of the Hausman test to semi-parametric and nonparametric panel data models. A formal application of the Hausman test is given focusing on testing between fixed and random effects within a panel data model. Mostly fixed effects are accepted way to run with panel data as they always present consistent outcomes but may not be the most effective way to implement. On the other hand, random effects usually provide to the researcher better P-values as it considered to be a more active estimator, so researcher can study random effects if it is reasonable to do so. Moreover, Hausman test choose a more effective model compared to a less efficient as consistent model should presents robust estimates and consistent results owing to the more efficient model. Autocorrelation test Another terms sometimes used for describe Autocorrelation these are “lagged

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