Lewis, Daniel J;
Melcangi, Davide;
Pilossoph, Laura;
Toner-Rodgers, Aidan;
(2022)
Approximating Grouped Fixed Effects Estimation via Fuzzy Clustering Regression.
(Staff Reports
1033).
Federal Reserve Bank of New York: New York, NY, USA.
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Abstract
We propose a new, computationally-efficient way to approximate the “grouped fixed-effects” (GFE) estimator of Bonhomme and Manresa (2015), which estimates grouped patterns of unobserved heterogeneity. To do so, we generalize the fuzzy C-means objective to regression settings. As the regularization parameter m approaches 1, the fuzzy clustering objective converges to the GFE objective; moreover, we recast this objective as a standard Generalized Method of Moments problem. We replicate the empirical results of Bonhomme and Manresa (2015) and show that our estimator delivers almost identical estimates. In simulations, we show that our approach delivers improvements in terms of bias, classification accuracy, and computational speed.
| Type: | Working / discussion paper |
|---|---|
| Title: | Approximating Grouped Fixed Effects Estimation via Fuzzy Clustering Regression |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.2139/ssrn.4232199 |
| Publisher version: | https://www.newyorkfed.org/research/staff_reports/... |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | clustering, unobserved heterogeneity, panel data |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL SLASH UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10173548 |
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