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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.

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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series Economics Working Papers with number 2006-08.

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Length: 39
Date of creation: 14 Jul 2006
Date of revision:
Handle: RePEc:aah:aarhec:2006-08

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Web page: http://www.econ.au.dk/afn/

Related research

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

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References

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Cited by:
  1. Peter Jensen, 2010. "Testing the null of a low dimensional growth model," Empirical Economics, Springer, vol. 38(1), pages 193-215, February.
  2. Capolupo, Rosa, 2008. "The New Growth Theories and Their Empirics after Twenty Years," Economics Discussion Papers 2008-27, Kiel Institute for the World Economy.
  3. Stanislav Anatolyev, 2009. "Inference in Regression Models with Many Regressors," Working Papers w0125, Center for Economic and Financial Research (CEFIR).

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