Regression Models with Data-based Indicator Variables
OLS 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 improvement. Finally, a possible modification to impulse intercept corrections is considered.
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- repec:cup:cbooks:9780521634809 is not listed on IDEAS
- Jurgen A. Doornik & David F. Hendry & Bent Nielsen, 1998.
"Inference in Cointegrating Models: UK M1 Revisited,"
Journal of Economic Surveys,
Wiley Blackwell, vol. 12(5), pages 533-572, December.
- Doornik, Jurgen A & Hendry, David F & Nielsen, Bent, 1998. " Inference in Cointegrating Models: UK M1 Revisited," Journal of Economic Surveys, Wiley Blackwell, vol. 12(5), pages 533-72, December.
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- Salkever, David S., 1976. "The use of dummy variables to compute predictions, prediction errors, and confidence intervals," Journal of Econometrics, Elsevier, vol. 4(4), pages 393-397, November.
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