There are much more serious matters to be concerned about! Similarly, the omission of relevant covariates "robust" standard errors is of second-order importance. More specifically, these results change (for the worse) in the context of such non-linear models as Logit, Probit, Tobit, and the various extensions of these models.įor example, in the presence of heteroskedasticity, the MLE's of the parameters of these models are inconsistent. These results change if the model is non-linear in the parameters - a fact that is well known ( e.g., Maddala & Nelson, 1975 Hurd, 1979 Īrabmazar & Schmidt, 1981 White, 1981 Lee, 1982 Ruud, 1983 Kiefer & Skoog, 1984 Yatchew & Griliches, 1985 Blundell, 1987), but largely ignored in most empirical studies. This bias vanishes if the included and excluded regressors are orthogonal (uncorrelated in the sample). Similarly, although the omission of relevant regressors from the standard linear regression model generally biases the OLS parameter estimates, Usual estimated covariance matrix for the OLS parameter estimator inconsistent, the parameter estimates themselves only lose efficiency - they are still unbiasedĪnd consistent. For example, although heteroskedasticity renders the In the linear regression model, certain type of mis-specification have only mild implications for our inferences. In Limited Dependent Variable Models Background EViews Workfiles & Program Files for Specification Testing in Limited Dependent Variable Models EViews Workfiles & Program Files for Specification Testing
0 kommentar(er)
