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On determining the importance of a regressor with small and undersized samples

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  • Peter Sandholt Jensen
  • Allan H. Würtz

    () (Department of Economics, University of Aarhus, Denmark)

Abstract

A 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.

Suggested Citation

  • Peter Sandholt Jensen & Allan H. Würtz, 2006. "On determining the importance of a regressor with small and undersized samples," Economics Working Papers 2006-08, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:aarhec:2006-08
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    File URL: ftp://ftp.econ.au.dk/afn/wp/06/wp06_08.pdf
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    References listed on IDEAS

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    1. Durlauf, Steven N. & Johnson, Paul A. & Temple, Jonathan R.W., 2005. "Growth Econometrics," Handbook of Economic Growth,in: Philippe Aghion & Steven Durlauf (ed.), Handbook of Economic Growth, edition 1, volume 1, chapter 8, pages 555-677 Elsevier.
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    Citations

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    Cited by:

    1. Capolupo, Rosa, 2009. "The New Growth Theories and Their Empirics after Twenty Years," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 3, pages 1-72.
    2. Peter Jensen, 2010. "Testing the null of a low dimensional growth model," Empirical Economics, Springer, vol. 38(1), pages 193-215, February.
    3. Anatolyev, Stanislav, 2012. "Inference in regression models with many regressors," Journal of Econometrics, Elsevier, vol. 170(2), pages 368-382.

    More about this item

    Keywords

    AIC; BACE; BIC; extreme bounds analysis; general-to-specific; identification; ill-posed inverse problem; robustness; sensitivity analysis;

    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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