Identification and Estimation of Categorical Random Coefficient Models

Arbeitspapier

This paper proposes a linear categorical random coefficient model, in which the random coefficients follow parametric categorical distributions. The distributional parameters are identified based on a linear recurrence structure of moments of the random coefficients. A Generalized Method of Moments estimator is proposed, and its finite sample properties are examined using Monte Carlo simulations. The utility of the proposed method is illustrated by estimating the distribution of returns to education in the U.S. by gender and educational levels. We find that rising heterogeneity between educational groups is mainly due to the increasing returns to education for those with postsecondary education, whereas within group heterogeneity has been rising mostly in the case of individuals with high school or less education.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: CESifo Working Paper ; No. 9714

Classification and Topics

• Classification: Wirtschaft ; Econometrics ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions ; Estimation: General ; Specific Distributions; Specific Statistics ; Wages, Compensation, and Labor Costs: General
• Subject: random coefficient models ; categorical distribution ; return to education

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Gao, Zhan ; Pesaran, M. Hashem
• Event: Veröffentlichung
• (who): Center for Economic Studies and ifo Institute (CESifo)
• (where): Munich
• (when): 2022

Further information

• Handle: http://hdl.handle.net/10419/260844
• Last update: 10.03.2025, 11:44 AM CET