tailieunhanh - Econometric theory and methods, Russell Davidson - Chapter 15

Chapter 15 Testing the Specification of Econometric Models Introduction As we first saw in Section , estimating a misspecified regression model generally yields biased and inconsistent parameter estimates. | Chapter 15 Testing the Specification of Econometric Models Introduction As we first saw in Section estimating a misspecified regression model generally yields biased and inconsistent parameter estimates. This is true for regression models whenever we incorrectly omit one or more regressors that are correlated with the regressors included in the model. Except in certain special cases some of which we have discussed it is also true for more general types of model and more general types of misspecification. This suggests that the specification of every econometric model should be thoroughly tested before we even tentatively accept its results. We have already discussed a large number of procedures that can be used as specification tests. These include t and F tests for omitted variables and for parameter constancy Section along with similar tests for nonlinear regression models Section and IV regression Section tests for het-eroskedasticity Section tests for serial correlation Section tests of common factor restrictions Section DWH tests Section tests of overidentifying restrictions Sections and and the three classical tests for models estimated by maximum likelihood notably LM tests Section . In this chapter we discuss a number of other procedures that are designed for testing the specification of econometric models. Some of these procedures explicitly involve testing a model against a less restricted alternative. Others do not make the alternative explicit and are intended to have power against a large number of plausible alternatives. In the next section we discuss a variety of tests that are based on artificial regressions. Then in Section we discuss nonnested hypothesis tests which are designed to test the specification of a model when alternative models are available. In Section we discuss model selection based on information criteria. Finally in Section we introduce the concept of .