On determining the importance of a regressor with small and undersized samples
AbstractA problem encountered in, for instance, growth empirics is that the number of explanatory variables is large compared to the number of observations. This makes it infeasible to condition on all variables in order to determine the importance of a variable of interest. We prove identifying assumptions under which the problem is not ill-posed. Under these assumptions, we derive properties of the most commonly used methods: Extreme bounds analysis, Sala-i-Martin’s method, BACE, generalto- specific, minimum t-statistics, BIC and AIC. We propose a new method and show that it has good finite sample properties.
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Bibliographic InfoPaper provided by School of Economics and Management, University of Aarhus in its series Economics Working Papers with number 2006-08.
Date of creation: 14 Jul 2006
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AIC; BACE; BIC; extreme bounds analysis; general-to-specific; identification; ill-posed inverse problem; robustness; sensitivity analysis;
Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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