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Omitted Variables Bias in Regime-Switching Models with Slope-Constrained Estimators: Evidence from Monte Carlo Simulations

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  • Ermanno Affuso
  • Steven B. Caudill
  • Franklin G. Mixon
  • Jr.

Abstract

In a recent study, Beccarini [1] showed that one can eliminate or reduce the bias in OLS regression estimators caused by an omitted polychotomous variable by estimating a regime-switching model. If the missing polychotomous variable assumes K values, then elimination or reduction of the bias requires the estimation of a K-component mixture model. In his Monte Carlo simulations, however, the slope of the parameter of interest is estimated once for each of the K components. After discussing problems associated with multiple estimates of the parameter of interest, this paper extends Beccarini’s Monte Carlo analysis to include the slope-constrained estimator obtained by using the EM algorithm of Bartolucci and Scaccia [2]. We find a small gain in efficiency with the slope-constrained estimator and that the weighted-average estimator in Beccarini [1] produces a large number of rejections of the true null hypothesis of a single slope when the components are not widely separated.

Suggested Citation

  • Ermanno Affuso & Steven B. Caudill & Franklin G. Mixon & Jr., 2013. "Omitted Variables Bias in Regime-Switching Models with Slope-Constrained Estimators: Evidence from Monte Carlo Simulations," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 2(3), pages 1-5.
    • Handle: RePEc:spt:stecon:v:2:y:2013:i:3:f:2_3_5
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