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Instrumental-variable estimation of large-T panel-data models with common factors

Author

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  • Sebastian Kripfganz

    (University of Exeter Business School)

  • Vasilis Sarafidis

    (BI Norwegian Business School)

Abstract

We introduce the xtivdfreg command in Stata, which implements a general instrumental variables (IV) approach for estimating panel data models with a large number of time series observations, T, and unobserved common factors or interactive effects, as developed by Norkute, Sarafidis, Yamagata, and Cui (2021, Journal of Econometrics) and Cui, Norkute, Sarafidis, and Yamagata (2020, ISER Discussion Paper). 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 (2SIV) 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 2SIV approach in two major ways. Firstly, the algorithm accommodates estimation of unbalanced panels. Secondly, the algorithm permits a flexible specification of instruments. It is shown 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.
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Suggested Citation

  • Sebastian Kripfganz & Vasilis Sarafidis, 2021. "Instrumental-variable estimation of large-T panel-data models with common factors," Economics Virtual Symposium 2021 3, Stata Users Group.
    • Handle: RePEc:boc:econ21:3
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    References listed on IDEAS

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    1. Norkutė, Milda & Sarafidis, Vasilis & Yamagata, Takashi & Cui, Guowei, 2021. "Instrumental variable estimation of dynamic linear panel data models with defactored regressors and a multifactor error structure," Journal of Econometrics, Elsevier, vol. 220(2), pages 416-446.
    2. Jan Ditzen, 2018. "Estimating dynamic common-correlated effects in Stata," Stata Journal, StataCorp LP, vol. 18(3), pages 585-617, September.
    3. Guowei Cui & Milda NorkutÄ— & Vasilis Sarafidis & Takashi Yamagata, 2022. "Two-stage instrumental variable estimation of linear panel data models with interactive effects [Eigenvalue ratio test for the number of factors]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 340-361.
    4. Sergio Correia, 2016. "reghdfe: Estimating linear models with multi-way fixed effects," 2016 Stata Conference 24, Stata Users Group.
    5. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    6. Sebastian Kripfganz & Vasilis Sarafidis, 2021. "Instrumental-variable estimation of large-T panel-data models with common factors," Stata Journal, StataCorp LP, vol. 21(3), pages 659-686, September.
    7. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
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