Identification and Estimation of Average Partial Effects in “Irregular” Correlated Random Coefficient Panel Data Models

By Bryan S. Graham, J. Powell. 2012

96 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/a41cf19807abc7caba0a8ebb5b5700689fd0526a

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Citations in the corpus

Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey (2021, background): “While it allows for some treatment eﬀect heterogeneity, that estimator relies on a linear treatment eﬀect assumption, like the estimator in Graham and Powell (2012). de Chaisemartin et al. (2022) show that after some relabelling, some of their estimators are equivalent or nearly equivalent to…”
A quantile correlated random coefficients panel data model (2016, methodology): “Our first estimand is the R × 1 vector of common coefficients δ (τ).”
Nonparametric identification in panels using quantiles (2013, methodology): “Graham and Powell (2012) consider a linear model with individual specific coefficients where φ(x, u) = β1(u) + β2(u)x in the scalar x case.”
Leave‐Out Estimation of Variance Components (2018, methodology): “Group memberships are often modeled as influencing slopes in addition to intercepts (Kuh, 1959; Hildreth and Houck, 1968; Raudenbush and Bryk, 2002; Arellano and Bonhomme, 2011; Graham and Powell, 2012; Graham et al., 2018).”
Sieve Wald and QLR Inferences on Semi/Nonparametric Conditional Moment Models (2014, unknown): “, North-Holland, Amsterdam, 6B. Chamberlain, G. (1992).”
Using mixtures in econometric models: a brief review and some new results (2016, background): “See Graham and Powell (2012) for further developments in identification and estimation in such settings.”
Average and Quantile Effects in Nonseparable Panel Models (2009, background): “Graham and Powell (2011) give estimators of the average effect in a linear model with heterogeneous slopes.”
IV Quantile Regression for Group-Level Treatments, with an Application to the Distributional Effects of Trade (2016, background): “7Our paper is also related to but di↵erent from Graham and Powell (2012) who studied the model that in our notation would take the form y ig = z0 ig ↵ g (u ig ) where u ig represents (potentially multi-dimensional) random unobserved heterogeneity, and developed an interesting identification and…”