Sebastian Kripfganz

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

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© Sebastian Kripfganz
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Title and Abstract
Instrumental variable estimation of large panel data models with common factors
This article introduces the xtivdfreg command in Stata, which implements a general instrumental variables (IV) approach for estimating large panel data models with unobserved common factors or interactive effects, as developed by Norkute et al. (2020) and Cui et al. (2020). The underlying idea of this approach is to project out the common factors from exogenous covariates using principal components analysis, and run IV regression using defactored covariates as instruments. The resulting "IVDF" method 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 IVDF approach in two major ways. Firstly, the algorithm accommodates estimation of unbalanced panels. Secondly, the algorithm permits highly flexible instrumentation strategies. It is shown that when one imposes zero factors, the xtivdfreg command can replicate the results of the popular ivregress Stata command. Notably, xtivdfreg also permits estimation of the two-way error components panel data model with heterogeneous slope coefficients.
Suggested Citation
Kripfganz, S., and V. Sarafidis (2020). Instrumental variable estimation of large panel data models with common factors. Working Paper, University of Exeter and Monash University.
Sebastian Kripfganz
University of Exeter
Vasilis Sarafidis
Monash University;
BI Norwegian Business School
Working Paper
Working Paper, University of Exeter and Monash University
(August 6, 2020)
Stata Program