nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2009‒04‒18
two papers chosen by
Philip Yu
Hong Kong University

  1. A Practitioner's Guide to Bayesian Estimation of Discrete Choice Dynamic Programming Models By Andrew Ching; Susumu Imai; Masakazu Ishihara; Neelam Jain
  2. A behavioral microsimulation model with discrete labour supply for Italian couples By Pacifico, Daniele

  1. By: Andrew Ching (University of Toronto); Susumu Imai (Queen's University); Masakazu Ishihara (University of Toronto); Neelam Jain (Northern Illinois University)
    Abstract: This paper provides a step-by-step guide to estimating discrete choice dynamic programming (DDP) models using the Bayesian Dynamic Programming algorithm developed by Imai Jain and Ching (2008) (IJC). The IJC method combines the DDP solution algorithm with the Bayesian Markov Chain Monte Carlo algorithm into a single algorithm, which solves the DDP model and estimates its structural parameters simultaneously. The main computational advantage of this estimation algorithm is the efficient use of information obtained from the past iterations. In the conventional Nested Fixed Point algorithm, most of the information obtained in the past iterations remains unused in the current iteration. In contrast, the Bayesian Dynamic Programming algorithm extensively uses the computational results obtained from the past iterations to help solving the DDP model at the current iterated parameter values. Consequently, it significantly alleviates the computational burden of estimating a DDP model. We carefully discuss how to implement the algorithm in practice, and use a simple dynamic store choice model to illustrate how to apply this algorithm to obtain parameter estimates.
    Keywords: Bayesian Dynamic Programming, Discrete Choice Dynamic Programming, Markov Chain Monte Carlo
    JEL: C11 M3
    Date: 2009–04
  2. By: Pacifico, Daniele
    Abstract: The aim of this paper is to introduce labour supply behaviour in an arithmetic microsimulation model so as to take into account changes in labour supply when a new policy is evaluated. I explore the performance of a labour supply estimation method based on a discrete choice set. The idea behind this approach is to work directly with preferences instead of labour supply functions. The main advantage of the discrete approach is the possibility of dealing easily with non-convex budget sets and joint labour supply. This let the discrete approach relatively suitable for policy evaluation purposes. I use the papers from Blundell, Dancan, McCrae and Meghir (1999) and Brewer, Duncan Shepard and Suarez (2006) as main references for the structural microeconometric model. Several innovative elements are taken into account with respect previous Italian studies. In particular, I allow for errors in the predicted wage for non-workers, unobserved heterogeneity in preferences, unobserved monetary fixed costs of working and child-care demand. The model is fully parametric and the Simulated Maximum Likelihood approach is used to approximate multidimensional integrals. An overview of the STATA routine for the maximum likelihood estimation is also presented. The elasticities of labour supply for married men and women are computed and discussed.
    Keywords: Microsimulation; Household labour supply; discrete choice
    JEL: H31 J22 H24
    Date: 2009–03

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