Estimating Spatial Dynamic Panel Data Models with Unobserved Common Factors in Stata
Sebastian Kripfganz & Vasilis Sarafidis
Abstract
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. Last, the spxtivdfreg package allows for heterogeneous slope coefficients, as in Chen, Cui, Sarafidis, and Yamagata (2025). In particular, we construct a
6 citations
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.44 × 0.4 = 0.18 |
| M · momentum | 0.65 × 0.15 = 0.10 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.