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 tdistribution, 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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Bibliographic InfoPaper provided by Economics Group, Nuffield College, University of Oxford in its series Economics Papers with number 2004-W04.
Length: 18 pages
Date of creation: 25 Feb 2004
Date of revision:
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Web page: http://www.nuff.ox.ac.uk/economics/
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 Hendry & Carlos Santos, 2003. "Regression Models with Data-based Indicator Variables," Economics Series Working Papers 2004-W04, University of Oxford, Department of Economics.
- David F. Hendry & Carlos Santos, 2004. "Regression Models with Data-based Indicator Variables," Economics Papers 2004-W13, 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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