Iteration Capping for Discrete Choice Models Using the EM Algorithm

Abstract

The Expectation-Maximization (EM) algorithm is a well-established estimation procedure which is used in many domains of econometric analysis. Recent application in a discrete choice framework (Train, 2008) facilitated estimation of latent class models allowing for very flexible treatment of unobserved heterogeneity. The high flexibility of these models is however counterweighted by often excessively long computation times, due to the iterative nature of the EM algorithm. This paper proposes a simple adjustment to the estimation procedure which proves to achieve substantial gains in terms of convergence speed without compromising any of the advantages of the original routine. The enhanced algorithm caps the number of iterations computed by the inner EM loop near its minimum, thereby avoiding optimization over suboptimally populated classes. Performance of the algorithm is assessed on a series of simulations, with the adjusted algorithm being 3-5 times faster than the original routine.JEL classification: C14, C63

The effectiveness of fiscal stimuli for working parents

“…For a discussion of the benefits of latent class models within the domain of structural labor supply modelling, see Apps et al (2012). For an overview of their implementation and potential computational improvements, see Kabátek (2013).…”

The Effectiveness of Fiscal Stimuli for Working Parents

“…For a discussion of the benefits of latent class models within the domain of structural labor supply modelling, see Apps et al (2012). For an overview of their implementation and potential computational improvements, see Kabátek (2013).…”