Grouped Patterns of Heterogeneity in Panel Data

By S. Bonhomme, Elena Manresa. 2015

457 citations recorded by the source corpus.

This paper introduces time‐varying grouped patterns of heterogeneity in linear panel data models. A distinctive feature of our approach is that group membership is left unrestricted. We estimate the parameters of the model using a “grouped fixed‐effects” estimator that minimizes a least squares criterion with respect to all possible groupings of the cross‐sectional units. Recent advances in the clustering literature allow for fast and efficient computation. We provide conditions under which our estimator is consistent as both dimensions of the panel tend to infinity, and we develop inference methods. Finally, we allow for grouped patterns of unobserved heterogeneity in the study of the link between income and democracy across countries.

Subfields: Economics and Econometrics, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/4cf1d5691c1ff84a90452619b83655e76811ffd4

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Citations in the corpus

Panel Data Models with Grouped Factor Structure Under Unknown Group Membership (2013, unknown): “This holds because N/T τ → 0 for τ > b. Theorem 2 is similar to a result obtained by Bonhomme and Manresa (2012).”
Sieve Estimation of Time-Varying Panel Data Models With Latent Structures (2019, methodology): “The latter approach was adopted by Lin and Ng (2012) in linear panel data models with additive fixed effects, by Bonhomme and Manresa (2015) for linear models with grouped additive effects, and by Ando and Bai (2016) in linear panel data models with grouped interactive fixed effects.”
On clustering categories of categorical predictors in generalized linear models (2021, background): “In many social science applications with individual-level data, categorical predictors representing group-membership, like individuals with the same country of origin, attending the same school or being treated at the same hospital, might be of interest (Bonhomme and Manresa, 2015; Johannemann et al”
Panel data quantile regression with grouped fixed effects (2018, methodology): “It has been successfully utilized in many economic applications, for instance Lin and Ng (2012), Bonhomme and Manresa (2015) and Ando and Bai (2016).”
Estimation and Inference about Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels. (2017, methodology): “ly -mixing) across time. Finally, we contribute to panel data literature (Mundlak (1978), Chamberlain (1982), Hahn and Kuersteiner (2001), Hahn and Kuersteiner (2002), Fernandez-Val and 8 Lee (2013), Bonhomme and Manresa (2015), Moon and Weidner (2015), Moon and Weidner (2016), Kock (2016b), Manresa”
Democracy Does Cause Growth (2019, unknown): “For instance, as elaborated further below, Bonhomme and Manresa (2015) find that, after conditioning on GDP, transitions to democracy are still significantly 16Although Przeworski et al. (2000) challenge the existence of democratization waves, the consensus in political science is that such waves…”
Estimation of Panel Group Structure Models with Structural Breaks in Group Memberships and Coefficients (2020, unknown): “Many studies have considered clustering individual units in panel data (Sun, 2015; Hahn and Moon, 2010; Lin and Ng, 2012; Bonhomme and Manresa, 2015; Su et al., 2016; Ando and Bai, 2016; Vogt and Linton, 2016; Wang et al., 2018, among others), all assuming that the group structure does not change…”
Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey (2021, background): “More closely related to our set-up, the impact of heterogeneous treatment eﬀects in the “group ﬁxed-eﬀects” model of Bonhomme and Manresa (2015) remains to be studied.”
A Distributional Framework for Matched Employer Employee Data (2019, methodology): “This discrete fixed-effects approach has recently been proposed in single-agent panel data analysis (Hahn and Moon, 2010; Lin and Ng, 2012; Bonhomme and Manresa, 2012).”
Fixed Effects Estimation of Large-TPanel Data Models (2018, background): “The estimated unobserved effects, usually called fixed effects, can be very noisy because there are few observations that are informative about them. Section 2 describes the incidental parameter problem in detail. Motivated by the first panel data sets available, the econometric theory was initially”
Inference for Iterated GMM Under Misspecification (2021, background): “The existence of the fixed point (10) has not been discussed in the previous literature.”
Determining the number of groups in latent panel structures with an application to income and democracy (2017, unknown): “The same idea can be applied to other group structures, such as those considered in Bonhomme and Manresa (2012) where xed e¤ects have a grouped pattern.”
Discretizing Unobserved Heterogeneity (2017, methodology): “To illustrate the performance of GFE in models where heterogeneity follows a nonlinear factor structure, we present the results of a small-scale simulation study based on our two examples (2) and (3).”
The Trade-off Between Income Inequality and Carbon Dioxide Emissions (2017, methodology): “Bonhomme and Manresa (2015) provide a useful alternative with their group fixed effects (GFE) model.”