nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2009‒05‒23
two papers chosen by
Philip Yu
Hong Kong University
  1. Participation in Higher Education: A Random Parameter Logit Approach with Policy Simulations By Flannery, Darragh; O'Donoghue, Cathal
  2. Improving the Numerical Performance of BLP Static and Dynamic Discrete Choice Random Coefficients Demand Estimation By Jean-Pierre H. Dubé; Jeremy T. Fox; Che-Lin Su
  1. By: Flannery, Darragh (National University of Ireland, Galway); O'Donoghue, Cathal (Teagasc Rural Economy Research Centre)
    Abstract: In this paper we present a theoretical model of higher education participation. We assume that young people that complete upper secondary education are faced with three choices, go to higher education, not go to higher education or go to higher education and work part time. Utilizing the Living in Ireland survey data 1994-2001 we model this choice in an Irish context by variation in costs (direct and indirect), the estimated lifecycle returns and household credit constraints. Using a random parameters logit choice model we find that simulated lifecycle earnings positively impact the educational/labour choices of young individuals in Ireland. This positive relationship is also found to be true for a choice-specific household income variable constructed in the paper. From the random parameters logit estimations we also find that preferences for choices with higher simulated lifecycle earnings and household income vary across individuals. We conduct policy simulations from our estimations and found that increasing student financial aid levels by 10% combined with a slight widening of the income limits for these aids can lead to significant movement away from the decision to not enter higher education.
    Keywords: higher education participation, random parameters logit model, lifecycle simulated earnings, higher education policy
    JEL: I23 C35
    Date: 2009–05
    URL: http://d.repec.org/n?u=RePEc:iza:izadps:dp4163&r=dcm
  2. By: Jean-Pierre H. Dubé; Jeremy T. Fox; Che-Lin Su
    Abstract: The widely-used estimator of Berry, Levinsohn and Pakes (1995) produces estimates of consumer preferences from a discrete-choice demand model with random coefficients, market-level demand shocks and endogenous prices. We derive numerical theory results characterizing the properties of the nested fixed point algorithm used to evaluate the objective function of BLP's estimator. We discuss problems with typical implementations, including cases that can lead to incorrect parameter estimates. As a solution, we recast estimation as a mathematical program with equilibrium constraints, which can be faster and which avoids the numerical issues associated with nested inner loops. The advantages are even more pronounced for forward-looking demand models where Bellman's equation must also be solved repeatedly. Several Monte Carlo and real-data experiments support our numerical concerns about the nested fixed point approach and the advantages of constrained optimization.
    JEL: C01 C61 L0
    Date: 2009–05
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:14991&r=dcm
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