nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2010‒02‒13
two papers chosen by
Philip Yu
Hong Kong University

  1. A Computationally Efficient Analytic Procedure for the Random Effects Probit Model By Peng-Hsuan Ke; Wen-Jen Tsay
  2. Euler-Equation Estimation for Discrete Choice Models: A Capital Accumulation Application By Russell Cooper; John Haltiwanger; Jonathan Willis

  1. By: Peng-Hsuan Ke (Institute of Economics, Academia Sinica, Taipei, Taiwan); Wen-Jen Tsay (Institute of Economics, Academia Sinica, Taipei, Taiwan)
    Abstract: It is found in Lee (2000) and Rabe-Hesketh et al. (2005) that the typical numerical-integral procedure suggested by Butler and Moffitt (1982) for the random effects probit model becomes biased when the correlation coefficient within each unit is relatively large. This could possibly explain why Guilkey and Murphy (1993, p. 316) recommend that if only two points (T=2) are available, then one may as well use the probit estimator. This paper tackles this issue by deriving an analytic formula for the likelihood function of the random effects probit model with T=2. Thus, the numerical-integral procedure is not required for the closed-form approach, and the possible bias generated from numerical integral is avoided. The simulation outcomes show that the root of mean-squared-error (RMSE) of the random effects probit estimator (MLE) using our method could be over 40% less than that from the probit estimator when the cross correlation is 0.9.
    Keywords: Discrete choice, random effects, panel probit model, error function
    JEL: C23 C24
    Date: 2010–01
  2. By: Russell Cooper; John Haltiwanger; Jonathan Willis
    Abstract: This paper studies capital adjustment at the establishment level. Our goal is to characterize capital adjustment costs, which are important for understanding both the dynamics of aggregate investment and the impact of various policies on capital accumulation. Our estimation strategy searches for parameters that minimize ex post errors in an Euler equation. This strategy is quite common in models for which adjustment occurs in each period. Here, we extend that logic to the estimation of parameters of dynamic optimization problems in which non-convexities lead to extended periods of investment inactivity. In doing so, we create a method to take into account censored observations stemming from intermittent investment. This methodology allows us to take the structural model directly to the data, avoiding time-consuming simulation based methods. To study the effectiveness of this methodology, we first undertake several Monte Carlo exercises using data generated by the structural model. We then estimate capital adjustment costs for U.S. manufacturing establishments in two sectors.
    Date: 2010–01

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