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On CCE estimation of factor-augmented models when regressors are not linear in the factors

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  • De Vos, Ignace
  • Westerlund, Joakim

Abstract

In empirical research it is often of interest to include non-linear functions of the explanatory variables, such as squares or interactions, in the specification. A popular technique to estimate such models in the presence of common factors is the Common Correlated Effects (CCE) methodology. However, this approach assumes that the regressors are linear in the factors, which is not the case if variables enter non-linearly. In this note we show how CCE should be implemented when some regressors violate the linear factor model assumption.

Suggested Citation

  • De Vos, Ignace & Westerlund, Joakim, 2019. "On CCE estimation of factor-augmented models when regressors are not linear in the factors," Economics Letters, Elsevier, vol. 178(C), pages 5-7.
    • Handle: RePEc:eee:ecolet:v:178:y:2019:i:c:p:5-7
    DOI: 10.1016/j.econlet.2019.02.001
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    References listed on IDEAS

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    3. Robertson, Donald & Sarafidis, Vasilis, 2015. "IV estimation of panels with factor residuals," Journal of Econometrics, Elsevier, vol. 185(2), pages 526-541.
    4. Ahn, Seung C. & Lee, Young H. & Schmidt, Peter, 2013. "Panel data models with multiple time-varying individual effects," Journal of Econometrics, Elsevier, vol. 174(1), pages 1-14.
    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. Ahn, Seung Chan & Hoon Lee, Young & Schmidt, Peter, 2001. "GMM estimation of linear panel data models with time-varying individual effects," Journal of Econometrics, Elsevier, vol. 101(2), pages 219-255, April.
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    Citations

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    Cited by:

    1. Stauskas, Ovidijus & De Vos, Ignace, 2024. "Handling Distinct Correlated Effects with CCE," MPRA Paper 120194, University Library of Munich, Germany.
    2. Artūras Juodis, 2022. "A regularization approach to common correlated effects estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 788-810, June.
    3. Nicholas L. Brown & Peter Schmidt & Jeffrey M. Wooldridge, 2021. "Simple Alternatives to the Common Correlated Effects Model," Papers 2112.01486, arXiv.org.
    4. Ulucak, Recep & Koçak, Emrah & Erdoğan, Seyfettin & Kassouri, Yacouba, 2020. "Investigating the non-linear effects of globalization on material consumption in the EU countries: Evidence from PSTR estimation," Resources Policy, Elsevier, vol. 67(C).
    5. Philip Kerner & Torben Klarl & Tobias Wendler, 2021. "Green Technologies, Environmental Policy and Regional Growth," Bremen Papers on Economics & Innovation 2104, University of Bremen, Faculty of Business Studies and Economics.
    6. Recep Ulucak & Danish & Yacouba Kassouri, 2020. "An assessment of the environmental sustainability corridor: Investigating the non‐linear effects of environmental taxation on CO2 emissions," Sustainable Development, John Wiley & Sons, Ltd., vol. 28(4), pages 1010-1018, July.
    7. Mauro, Luciano & Pigliaru, Francesco & Carmeci, Gaetano, 2023. "Decentralization, social capital, and regional growth: The case of the Italian North-South divide," European Journal of Political Economy, Elsevier, vol. 78(C).
    8. Mohitosh Kejriwal & Xiaoxiao Li & Linh Nguyen & Evan Totty, 2024. "The efficacy of ability proxies for estimating the returns to schooling: A factor model‐based evaluation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 3-21, January.
    9. Yan Sun & Wei Huang, 2022. "Quasi-maximum likelihood estimation of short panel data models with time-varying individual effects," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(1), pages 93-114, January.
    10. Christis Katsouris, 2023. "Optimal Estimation Methodologies for Panel Data Regression Models," Papers 2311.03471, arXiv.org, revised Nov 2023.

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    More about this item

    Keywords

    CCE; Factor-augmented regression models; Non-linear regressors;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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