Regression Models with Data-based Indicator Variables
AbstractOLS 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 Whiteâ€™s (1980) heteroskedasticity test is incorrectly sized in that context, we suggest an improvement. Finally, a possible modification to impulse â€˜intercept correctionsâ€™ is considered.
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Bibliographic InfoPaper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 2004-W04.
Date of creation: 01 Nov 2003
Date of revision:
Other versions of this item:
- David F. Hendry & Carlos Santos, 2005. "Regression Models with Data-based Indicator Variables," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(5), pages 571-595, October.
- David F. Hendry & Carlos Santos, 2004. "Regression Models with Data-based Indicator Variables," Economics Papers 2004-W13, Economics Group, Nuffield College, University of Oxford.
- David F. Hendry & Carlos Santos, 2004. "Regression Models with Data-based Indicator Variables," Economics Papers 2004-W04, Economics Group, Nuffield College, University of Oxford.
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
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