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Estimating the effect of a variable in a high-dimensional regression model


  • Peter Sandholt Jensen

    () (Department of Business and Economics, University of Southern Denmark)

  • Allan H. Würtz

    () (School of Economics and Management, University of Aarhus and CREATES)


A problem encountered in some empirical research, e.g. growth empirics, is that the potential 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 whether a particular variable has an effect. We assume that the effect is identified in a high-dimensional linear model specified by unconditional moment restrictions. We consider properties of the following methods, which rely on lowdimensional models to infer the effect: Extreme bounds analysis, the minimum t-statistic over models, Sala-i-Martin’s method, BACE, BIC, AIC and general-tospecific. We propose a new method and show that it is well behaved compared to existing methods.

Suggested Citation

  • Peter Sandholt Jensen & Allan H. Würtz, 2010. "Estimating the effect of a variable in a high-dimensional regression model," CREATES Research Papers 2010-73, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2010-73

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    References listed on IDEAS

    1. Xavier Sala-I-Martin & Gernot Doppelhofer & Ronald I. Miller, 2004. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," American Economic Review, American Economic Association, vol. 94(4), pages 813-835, September.
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    More about this item


    AIC; BACE; BIC; extreme bounds analysis; general-to-specific; 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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