Sebastian Kripfganz

Senior Lecturer (advanced assistant professor) in Econometrics,
University of Exeter Business School, Department of Economics

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Title and Abstract
Instrumental-variable estimation of large-T panel-data models with common factors
This article introduces the xtivdfreg command in Stata, which implements a general instrumental-variables (IV) approach for estimating panel-data models with many time series observations, T, and unobserved common factors or interactive effects, as developed by Norkute et al. (2021, Journal of Econometrics 220: 416-446) and Cui et al. (2020, ISER Discussion Paper 1101). The underlying idea of this approach is to project out the common factors from exogenous covariates using principal-components analysis, and to run IV regression in both of two stages, using defactored covariates as instruments. The resulting two-stage IV estimator is valid for models with homogeneous or heterogeneous slope coefficients and has several advantages relative to existing popular approaches. In addition, the xtivdfreg command extends the two-stage IV approach in two major ways. First, the algorithm accommodates estimation of unbalanced panels. Second, the algorithm permits a flexible specification of instruments. We show that when one imposes zero factors, the xtivdfreg command can replicate the results of the popular ivregress Stata command. Notably, unlike ivregress, xtivdfreg permits estimation of the two-way error-components panel-data model with heterogeneous slope coefficients.
Suggested Citation
Kripfganz, S., and V. Sarafidis (2021). Instrumental-variable estimation of large-T panel-data models with common factors. Stata Journal 21 (3), 659-686.
Related Work
Kripfganz, S., and V. Sarafidis (2024). Estimating spatial dynamic panel data models with unobserved common factors in Stata. Journal of Statistical Software, forthcoming.
Sebastian Kripfganz
University of Exeter
Vasilis Sarafidis
BI Norwegian Business School;
Monash University
Journal Article
Stata Journal 21 (3), 659-686
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