Identification and Estimation of Categorical Random Coefficient Models

Zhan Gao and Mohammad Pesaran

No 9714, CESifo Working Paper Series from CESifo

Abstract: 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.

Keywords: random coefficient models; categorical distribution; return to education (search for similar items in EconPapers)
JEL-codes: C01 C13 C21 C46 J30 (search for similar items in EconPapers)
Date: 2022
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-lma
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https://www.cesifo.org/DocDL/cesifo1_wp9714.pdf (application/pdf)

Related works:
Journal Article: Identification and estimation of categorical random coefficient models (2023)
Working Paper: Identification and Estimation of Categorical Random Coefficient Models (2023)
Working Paper: Identification and Estimation of Categorical Random Coeficient Models (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_9714

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