Regression Models with Data-based Indicator Variables

Ordinary least squares estimation of an impulse-indicator coefficient is inconsistent, but its variance can be consistently estimated. Although the ratio of the inconsistent estimator to its standard error has a "t"-distribution, that test is inconsistent: one solution is to form an index of indicators. We provide Monte Carlo evidence that including a plethora of indicators need not distort model selection, permitting the use of many dummies in a general-to-specific framework. Although <link rid="b22">White's (1980)</link> heteroskedasticity test is incorrectly sized in that context, we suggest an easy alteration. Finally, a possible modification to impulse 'intercept corrections' is considered. Copyright 2005 Blackwell Publishing Ltd.