eprintid: 48655
rev_number: 7
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datestamp: 2024-02-22 14:44:19
lastmod: 2024-04-30 12:42:07
status_changed: 2024-04-30 12:42:07
type: article
succeeds: 43575
metadata_visibility: show
creators_name: Beyhum, Jad
creators_name: Gautier, Éric
creators_idrefppn: 255581564
creators_idrefppn: 168412969
creators_halaffid: 1002422
title: Factor and Factor Loading Augmented Estimators for Panel Regression With Possibly Nonstrong Factors
ispublished: pub
subjects: subjects_ECO
abstract: This article considers linear panel data models where the dependence of the regressors and the unobservables is modeled through a factor structure. The number of time periods and the sample size both go to infinity. Unlike in most existing methods for the estimation of this type of models, nonstrong factors are allowed and the number of factors can grow to infinity with the sample size. We study a class of two-step estimators of the regression coefficients. In the first step, factors and factor loadings are estimated. Then, the second step corresponds to the panel regression of the outcome on the regressors and the estimates of the factors and the factor loadings from the first step. The estimators enjoy double robustness. Different methods can be used in the first step while the second step is unique. We derive sufficient conditions on the first-step estimator and the data generating process under which the two-step estimator is asymptotically normal. Assumptions under which using an approach based on principal components analysis in the first step yields an asymptotically normal estimator are also given. The two-step procedure exhibits good finite sample properties in simulations. The approach is illustrated by an empirical application on fiscal policy.
date: 2023
date_type: published
publisher: American Statistical Association
id_number: 10.1080/07350015.2021.2011300
official_url: http://tse-fr.eu/pub/129095
faculty: tse
divisions: tse
keywords: Factor models
keywords: Flexible unobserved heterogeneity
keywords: Interactive fixed effects
keywords: Panel data
keywords: Principal components analysis
language: en
has_fulltext: FALSE
doi: 10.1080/07350015.2021.2011300
view_date_year: 2023
full_text_status: none
publication: Journal of Business and Economic Statistics
volume: vol. 41
number: n° 1
pagerange: 270-281
refereed: TRUE
issn: 0735-0015
oai_identifier: oai:tse-fr.eu:129095
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oai_lastmod: 2024-04-29T08:35:08Z
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site: ut1
hal_id: hal-04473333
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hal_url: https://hal.science/hal-04473333
citation:   Beyhum, Jad <https://www.idref.fr/255581564> and Gautier, Éric <https://www.idref.fr/168412969>  (2023) Factor and Factor Loading Augmented Estimators for Panel Regression With Possibly Nonstrong Factors.    Journal of Business and Economic Statistics, vol. 41 (n° 1).  pp. 270-281.