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A Classifier-Lasso Approach for Estimating Production Functions with Latent Group Structures

Daniel Czarnowske

arXiv 4 Mar 2022 · Econometrics · 1 citations (OpenAlex)

arXiv:2203.02220 · PDF · DOI · OpenAlex · Extracted main text

Abstract

I present a new estimation procedure for production functions with latent group structures. I consider production functions that are heterogeneous across groups but time-homogeneous within groups, and where the group membership of the firms is unknown. My estimation procedure is fully data-driven and embeds recent identification strategies from the production function literature into the classifier-Lasso. Simulation experiments demonstrate that firms are assigned to their correct latent group with probability close to one. I apply my estimation procedure to a panel of Chilean firms and find sizable differences in the estimates compared to the standard approach of classification by industry.

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Su, Liangjun, Shi, Zhentao, Phillips, Peter C. B (2016) Identifying Latent Structures in Panel Data1.000154100%
2Gandhi, Amit, Navarro, Salvador, Rivers, David A (2020) On the Identification of Gross Output Production Functions1.000134100%
3Ackerberg, Daniel A., Caves, Kevin, Frazer, Garth (2015) Identification Properties of Recent Production Function Estimators1.00053100%
4Olley, G. Steven, Pakes, Ariel (1996) The Dynamics of Productivity in the Telecommunications Equipment Industry0.84333100%
5Su, Liangjun, Wang, Xia, Jin, Sainan (2019) Sieve Estimation of Time-Varying Panel Data Models With Latent Structures0.84333100%
6Blundell, Richard, Bond, Stephen (2000) GMM Estimation with persistent panel data: an application to production functions0.81142100%
7Arellano, Manuel, Bond, Stephen (1991) Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations0.64422100%
8Bai, Jushan, Ng, Serena (2002) Determining the Number of Factors in Approximate Factor Models0.64422100%
9Griliches, Zvi, Mairesse, Jacques, Strøm, Steinar (1999) Production Functions: The Search for Identification0.64422100%
10Kasahara, Hiroyuki, Schrimpf, Paul, Suzuki, Michio (2017) Identification and Estimation of Production Function with Unobserved Heterogeneity0.64422100%

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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Online appendix to “Latent group structure in linear panel data models with endogenous regressors”0.40511