Sebastian Kripfganz

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

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© Sebastian Kripfganz
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Title and Abstract
Estimating spatial dynamic panel data models with unobserved common factors in Stata
This article introduces the spxtivdfreg package in Stata, which implements a general instrumental variables (IV) approach for estimating dynamic spatial panel data models with unobserved common factors or interactive effects, when the number of both cross-sectional and time series observations is large. The estimator has been developed in a recent paper by Cui, Sarafidis, and Yamagata (2023). The underlying idea 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 (and their spatial counterparts) as instruments. The resulting two-stage IV estimator is valid for models with homogeneous slope coefficients, and has several advantages relative to existing popular approaches. In addition, the spxtivdfreg package allows estimation of short-run and long-run direct and indirect effects, as well as total effects, accounting for the cumulative effects over time and across space. Standard errors for such effects are computed using the Delta method.
Suggested Citation
Kripfganz, S., and V. Sarafidis (2023). Estimating spatial dynamic panel data models with unobserved common factors in Stata. Manuscript, University of Exeter and Brunel University London.
Related Work
Kripfganz, S., and V. Sarafidis (2021). Instrumental-variable estimation of large-T panel-data models with common factors. Stata Journal 21 (3), 659-686.
Sebastian Kripfganz
University of Exeter
Vasilis Sarafidis
BI Norwegian Business School;
Brunel University London
Manuscript (July 19, 2023)
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