Abstract
Heteroscedasticity and autocorrelation typically are assumed to be absent in econometric models. Linear regression models are forgiving if these assumptions fail: ordinary least squares (OLS) estimates remain consistent if errors are not homoscedastic or are autocorrelated. Estimators for models with discrete data are not always as forgiving as OLS. For example, the Standard probit estimator continues to provide consistent estimates when error terms are autocorrelated, but the estimates are inconsistent as well as inefficient when errors have non-constant variances. Failure of the homoscedasticity assumption also leads to inconsistent estimates in such common models as tobit and logit. Thus, heteroscedasticity is a serious problem in models with discrete data.
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McMillen, D.P. (1995). Spatial Effects in Probit Models: A Monte Carlo Investigation. In: Anselin, L., Florax, R.J.G.M. (eds) New Directions in Spatial Econometrics. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-79877-1_9
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