Approximating Grouped Fixed Effects Estimation via Fuzzy Clustering Regression
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Other versions of this item:
- Daniel J. Lewis & Davide Melcangi & Laura Pilossoph & Aidan Toner‐Rodgers, 2023. "Approximating grouped fixed effects estimation via fuzzy clustering regression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(7), pages 1077-1084, November.
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Cited by:
- Daniel J. Lewis & Davide Melcangi & Laura Pilossoph, 2019.
"Latent Heterogeneity in the Marginal Propensity to Consume,"
Staff Reports
902, Federal Reserve Bank of New York.
- Daniel Lewis & Davide Melcangi & Laura Pilossoph, 2024. "Latent heterogeneity in the marginal propensity to consume," CeMMAP working papers 13/24, Institute for Fiscal Studies.
- Daniel Lewis & Davide Melcangi & Laura Pilossoph, 2019. "Latent Heterogeneity in the Marginal Propensity to Consume," 2019 Meeting Papers 519, Society for Economic Dynamics.
- Daniel Lewis & Davide Melcangi & Laura Pilossoph, 2024. "Latent Heterogeneity in the Marginal Propensity to Consume," NBER Working Papers 32523, National Bureau of Economic Research, Inc.
- Mugnier, Martin, 2025. "A simple and computationally trivial estimator for grouped fixed effects models," Journal of Econometrics, Elsevier, vol. 250(C).
- Millimet, Daniel L. & Bellemare, Marc, 2023. "Fixed Effects and Causal Inference," IZA Discussion Papers 16202, Institute of Labor Economics (IZA).
- Pionati, Alessandro, 2025. "Latent grouped structures in panel data: a review," MPRA Paper 123954, University Library of Munich, Germany.
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Keywords
; ; ;JEL classification:
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
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This paper has been announced in the following NEP Reports:- NEP-ECM-2022-10-17 (Econometrics)
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