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 |
URL: |
http://d.repec.org/n?u=RePEc:sin:wpaper:10-a001&r=dcm |