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Low sample size and regression: A Monte Carlo approach

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  • Riveros Gavilanes, John Michael

Abstract

This article performs simulations with different small samples considering the regression techniques of OLS, Jackknife, Bootstrap, Lasso and Robust Regression in order to stablish the best approach in terms of lower bias and statistical significance with a pre-specified data generating process -DGP-. The methodology consists of a DGP with 5 variables and 1 constant parameter which was regressed among the simulations with a set of random normally distributed variables considering samples sizes of 6, 10, 20 and 500. Using the expected values discriminated by each sample size, the accuracy of the estimators was calculated in terms of the relative bias for each technique. The results indicate that Jackknife approach is more suitable for lower sample sizes as it was stated by Speed (1994), Bootstrap approach reported to be sensitive to a lower sample size indicating that it might not be suitable for stablish significant relationships in the regressions. The Monte Carlo simulations also reflected that when a significant relationship is found in small samples, this relationship will also tend to remain significant when the sample size is increased.

Suggested Citation

  • Riveros Gavilanes, John Michael, 2019. "Low sample size and regression: A Monte Carlo approach," MPRA Paper 97017, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:97017
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    File URL: https://mpra.ub.uni-muenchen.de/97017/7/MPRA_paper_97017.pdf
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    More about this item

    Keywords

    Small sample size; Statistical significance; Regression; Simulations; Bias;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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