Biases in the Maximum Simulated Likelihood Estimation of the Mixed Logit Model

Maksat Jumamyradov (), Murat Munkin, William H. Greene and Benjamin M. Craig
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Maksat Jumamyradov: Department of Economics, University of South Florida, Tampa, FL 33620, USA
William H. Greene: Department of Economics, University of South Florida, Tampa, FL 33620, USA
Benjamin M. Craig: Department of Economics, University of South Florida, Tampa, FL 33620, USA

Econometrics, 2024, vol. 12, issue 2, 1-15

Abstract: In a recent study, it was demonstrated that the maximum simulated likelihood (MSL) estimator produces significant biases when applied to the bivariate normal and bivariate Poisson-lognormal models. The study’s conclusion suggests that similar biases could be present in other models generated by correlated bivariate normal structures, which include several commonly used specifications of the mixed logit (MIXL) models. This paper conducts a simulation study analyzing the MSL estimation of the error components (EC) MIXL. We find that the MSL estimator produces significant biases in the estimated parameters. The problem becomes worse when the true value of the variance parameter is small and the correlation parameter is large in magnitude. In some cases, the biases in the estimated marginal effects are as large as 12% of the true values. These biases are largely invariant to increases in the number of Halton draws.

Keywords: maximum simulated likelihood; mixed logit; discrete choice models (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2024
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