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Regression Models with Data‐based Indicator Variables *
Author(s) -
Hendry David F.,
Santos Carlos
Publication year - 2005
Publication title -
oxford bulletin of economics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.131
H-Index - 73
eISSN - 1468-0084
pISSN - 0305-9049
DOI - 10.1111/j.1468-0084.2005.00132.x
Subject(s) - heteroscedasticity , ordinary least squares , econometrics , estimator , statistics , monte carlo method , context (archaeology) , variance (accounting) , standard error , model selection , mathematics , computer science , economics , paleontology , accounting , biology
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 White's (1980) heteroskedasticity test is incorrectly sized in that context, we suggest an easy alteration. Finally, a possible modification to impulse ‘intercept corrections’ is considered.