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Policy Evaluation with Interactive Fixed Effects

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  • Chan, Mark K.
  • Kwok, Simon

Abstract

We develop an alternative estimator for policy evaluation in the presence of interactive fixed effects. It extends Pesaran (2006)’s two-stage procedure to a difference-in- differences-type program evaluation framework, and extracts principal components from the control group to form factor proxies. Consistency and asymptotic distributions are derived under stationary factors, as well as nonstationary factors with any integration order. Simulation exercises demonstrate excellent performance of our estimator relative to existing methods. We present empirical results from microeconomic and macroeconomic applications. We find that our estimator generates the most robust treatment effect estimates, and our weights for control group units deliver strong economic interpretation regarding the nature of the underlying factors.

Suggested Citation

  • Chan, Mark K. & Kwok, Simon, 2016. "Policy Evaluation with Interactive Fixed Effects," Working Papers 2016-11, University of Sydney, School of Economics.
    • Handle: RePEc:syd:wpaper:2016-11
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    References listed on IDEAS

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    1. Gobillon, Laurent & Magnac, Thierry & Selod, Harris, 2012. "Do unemployed workers benefit from enterprise zones? The French experience," Journal of Public Economics, Elsevier, vol. 96(9-10), pages 881-892.
    2. Kapetanios, G. & Pesaran, M. Hashem & Yamagata, T., 2011. "Panels with non-stationary multifactor error structures," Journal of Econometrics, Elsevier, vol. 160(2), pages 326-348, February.
    3. Holly, Sean & Pesaran, M. Hashem & Yamagata, Takashi, 2010. "A spatio-temporal model of house prices in the USA," Journal of Econometrics, Elsevier, vol. 158(1), pages 160-173, September.
    4. Laurent Gobillon & Thierry Magnac, 2016. "Regional Policy Evaluation: Interactive Fixed Effects and Synthetic Controls," The Review of Economics and Statistics, MIT Press, vol. 98(3), pages 535-551, July.
    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. Bai, Jushan & Kao, Chihwa & Ng, Serena, 2009. "Panel cointegration with global stochastic trends," Journal of Econometrics, Elsevier, vol. 149(1), pages 82-99, April.
    7. Hyungsik Roger Moon & Martin Weidner, 2015. "Linear Regression for Panel With Unknown Number of Factors as Interactive Fixed Effects," Econometrica, Econometric Society, vol. 83(4), pages 1543-1579, July.
    8. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
    9. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    10. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
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    Cited by:

    1. Pantelis Samartsidis & Shaun R. Seaman & Silvia Montagna & André Charlett & Matthew Hickman & Daniela De Angelis, 2020. "A Bayesian multivariate factor analysis model for evaluating an intervention by using observational time series data on multiple outcomes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1437-1459, October.
    2. Hyungsik Roger Moon & Martin Weidner, 2018. "Nuclear Norm Regularized Estimation of Panel Regression Models," Papers 1810.10987, arXiv.org, revised Jun 2023.
    3. Chan, Marc K. & Kwok, Simon, 2018. "Connecting the markets? Recent evidence on China’s capital account liberalization," Economic Modelling, Elsevier, vol. 70(C), pages 417-428.
    4. Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2019. "Inference on average treatment effects in aggregate panel data settings," CeMMAP working papers CWP32/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2018. "A $t$-test for synthetic controls," Papers 1812.10820, arXiv.org, revised Jan 2024.

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    Keywords

    Program evaluation; Interactive fixed effects; Difference-in-differences;
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