TY - JOUR
T1 - Censored Regression Estimation Under Unobserved Heterogeneity: A Stochastic Parameter Approach
AU - Ioannatos, Petros E.
PY - 1995/1/1
Y1 - 1995/1/1
N2 - This paper presents a methodology for addressing the problem of unobserved heterogeneity in the context of regression models based on censored samples. The effectiveness, feasibility, and usefulness of the proposed approach is illustrated by means of an empirical application as well as a simulation experiment. The paper demonstrates that censored regressions that control for the presence of unobserved heterogeneity perform substantially better in comparison to their counterparts in which the problem of unobserved heterogeneity is ignored.
AB - This paper presents a methodology for addressing the problem of unobserved heterogeneity in the context of regression models based on censored samples. The effectiveness, feasibility, and usefulness of the proposed approach is illustrated by means of an empirical application as well as a simulation experiment. The paper demonstrates that censored regressions that control for the presence of unobserved heterogeneity perform substantially better in comparison to their counterparts in which the problem of unobserved heterogeneity is ignored.
KW - Foreign direct investment
KW - Maximum likelihood
KW - Nonnested hypothesis testing
KW - Semiparametric estimation
KW - Simulation experiment
KW - Tobit model
UR - https://digitalcommons.kettering.edu/liberalstudies_facultypubs/4
UR - https://www.tandfonline.com/doi/abs/10.1080/07350015.1995.10524606
U2 - 10.1080/07350015.1995.10524606
DO - 10.1080/07350015.1995.10524606
M3 - Article
VL - 13
JO - Journal of Business and Economic Statistics
JF - Journal of Business and Economic Statistics
ER -