IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v206y2025ics0047259x24001003.html
   My bibliography  Save this article

Efficient estimation of a partially linear panel data model with cross-sectional dependence

Author

Listed:
  • Soberon, Alexandra
  • Mazzanti, Massimiliano
  • Musolesi, Antonio
  • Rodriguez-Poo, Juan M.

Abstract

This paper considers efficiency improvements in a partially linear panel data model that accounts for possible nonlinear effects of common covariates and allows for cross-sectional dependence arising simultaneously from unobserved common factors and spatial dependence. A generalized least squares-type estimator is proposed by taking into account this dependence structure. Also, possible gains in terms of the rate of convergence are studied. A Monte Carlo study is carried out to investigate the proposed estimators’ finite sample performance. Further, an empirical application is conducted to assess the impact of the carbon price linked to the European Union Emission Trading System on carbon dioxide emissions.

Suggested Citation

  • Soberon, Alexandra & Mazzanti, Massimiliano & Musolesi, Antonio & Rodriguez-Poo, Juan M., 2025. "Efficient estimation of a partially linear panel data model with cross-sectional dependence," Journal of Multivariate Analysis, Elsevier, vol. 206(C).
    • Handle: RePEc:eee:jmvana:v:206:y:2025:i:c:s0047259x24001003
    DOI: 10.1016/j.jmva.2024.105393
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047259X24001003
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jmva.2024.105393?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2016. "Exponent of Cross‐Sectional Dependence: Estimation and Inference," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 929-960, September.
    2. Daniel L. Millimet & John A. List & Thanasis Stengos, 2003. "The Environmental Kuznets Curve: Real Progress or Misspecified Models?," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 1038-1047, November.
    3. A. Denny Ellerman & Claudio Marcantonini & Aleksandar Zaklan, 2016. "The European Union Emissions Trading System: Ten Years and Counting," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 10(1), pages 89-107.
    4. Cem Ertur & Antonio Musolesi, 2017. "Weak and Strong Cross‐Sectional Dependence: A Panel Data Analysis of International Technology Diffusion," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 477-503, April.
    5. Raphael Calel, 2020. "Adopt or Innovate: Understanding Technological Responses to Cap-and-Trade," American Economic Journal: Economic Policy, American Economic Association, vol. 12(3), pages 170-201, August.
    6. Simon Reese & Joakim Westerlund, 2016. "Panicca: Panic on Cross‐Section Averages," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 961-981, September.
    7. Raphael Calel & Antoine Dechezleprêtre, 2016. "Environmental Policy and Directed Technological Change: Evidence from the European Carbon Market," The Review of Economics and Statistics, MIT Press, vol. 98(1), pages 173-191, March.
    8. Donald W. K. Andrews, 2005. "Cross-Section Regression with Common Shocks," Econometrica, Econometric Society, vol. 73(5), pages 1551-1585, September.
    9. Wagner, Martin & Grabarczyk, Peter & Hong, Seung Hyun, 2020. "Fully modified OLS estimation and inference for seemingly unrelated cointegrating polynomial regressions and the environmental Kuznets curve for carbon dioxide emissions," Journal of Econometrics, Elsevier, vol. 214(1), pages 216-255.
    10. Diego R. Känzig & Maximilian Konradt, 2024. "Climate Policy and the Economy: Evidence from Europe’s Carbon Pricing Initiatives," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 72(3), pages 1081-1124, September.
    11. Pesaran, M. Hashem & Tosetti, Elisa, 2011. "Large panels with common factors and spatial correlation," Journal of Econometrics, Elsevier, vol. 161(2), pages 182-202, April.
    12. M. Hashem Pesaran, 2021. "General diagnostic tests for cross-sectional dependence in panels," Empirical Economics, Springer, vol. 60(1), pages 13-50, January.
    13. Musolesi Antonio & Mazzanti Massimiliano, 2014. "Nonlinearity, heterogeneity and unobserved effects in the carbon dioxide emissions-economic development relation for advanced countries," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(5), pages 521-541, December.
    14. Koch, Nicolas & Themann, Michael, 2022. "Catching up and falling behind: Cross-country evidence on the impact of the EU ETS on firm productivity," Resource and Energy Economics, Elsevier, vol. 69(C).
    15. Jushan Bai & Serena Ng, 2004. "A PANIC Attack on Unit Roots and Cointegration," Econometrica, Econometric Society, vol. 72(4), pages 1127-1177, July.
    16. Badi H. Baltagi & Georges Bresson & Jean-Michel Etienne, 2019. "Carbon Dioxide Emissions and Economic Activities: A Mean Field Variational Bayes Semiparametric Panel Data Model with Random Coefficients," Annals of Economics and Statistics, GENES, issue 134, pages 43-77.
    17. Cole, Matthew A. & Elliott, Robert J.R. & Shimamoto, Kenichi, 2005. "Industrial characteristics, environmental regulations and air pollution: an analysis of the UK manufacturing sector," Journal of Environmental Economics and Management, Elsevier, vol. 50(1), pages 121-143, July.
    18. Zvi Griliches, 1998. "Patent Statistics as Economic Indicators: A Survey," NBER Chapters, in: R&D and Productivity: The Econometric Evidence, pages 287-343, National Bureau of Economic Research, Inc.
    19. Su, Liangjun & Jin, Sainan, 2010. "Profile quasi-maximum likelihood estimation of partially linear spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 157(1), pages 18-33, July.
    20. Papież, Monika & Śmiech, Sławomir & Frodyma, Katarzyna, 2022. "Does the European Union energy policy support progress in decoupling economic growth from emissions?," Energy Policy, Elsevier, vol. 170(C).
    21. Massimiliano Mazzanti & Antonio Musolesi, 2013. "The heterogeneity of carbon Kuznets curves for advanced countries: comparing homogeneous, heterogeneous and shrinkage/Bayesian estimators," Applied Economics, Taylor & Francis Journals, vol. 45(27), pages 3827-3842, September.
    22. Natalia Bailey & Sean Holly & M. Hashem Pesaran, 2016. "A Two‐Stage Approach to Spatio‐Temporal Analysis with Strong and Weak Cross‐Sectional Dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 249-280, January.
    23. Churchill, Sefa Awaworyi & Inekwe, John & Ivanovski, Kris & Smyth, Russell, 2018. "The Environmental Kuznets Curve in the OECD: 1870–2014," Energy Economics, Elsevier, vol. 75(C), pages 389-399.
    24. Hamilton, James D., 2003. "What is an oil shock?," Journal of Econometrics, Elsevier, vol. 113(2), pages 363-398, April.
    25. M. Hashem Pesaran, 2015. "Testing Weak Cross-Sectional Dependence in Large Panels," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 1089-1117, December.
    26. Badi H. Baltagi & Georges Bresson & Alain Pirotte, 2004. "Tobin q: Forecast performance for hierarchical Bayes, shrinkage, heterogeneous and homogeneous panel data estimators," Empirical Economics, Springer, vol. 29(1), pages 107-113, January.
    27. Giovanni Marin & Marianna Marino & Claudia Pellegrin, 2018. "The Impact of the European Emission Trading Scheme on Multiple Measures of Economic Performance," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(2), pages 551-582, October.
    28. Giovanni Marin & Marianna Marino & Claudia Pellegrin, 2018. "The impact of the European Union Emission Trading Scheme on Multiple Measures of Economic Performance," Post-Print hal-01768870, HAL.
    29. Xiao Huang, 2013. "Nonparametric Estimation in Large Panels with Cross-Sectional Dependence," Econometric Reviews, Taylor & Francis Journals, vol. 32(5-6), pages 754-777, August.
    30. Keane, Michael P & Prasad, Eswar S, 1996. "The Employment and Wage Effects of Oil Price Changes: A Sectoral Analysis," The Review of Economics and Statistics, MIT Press, vol. 78(3), pages 389-400, August.
    31. Lee, Jungyoon & Robinson, Peter, 2015. "Panel nonparametric regression with fixed effects," LSE Research Online Documents on Economics 61431, London School of Economics and Political Science, LSE Library.
    32. Hansen, Bruce E., 2008. "Uniform Convergence Rates For Kernel Estimation With Dependent Data," Econometric Theory, Cambridge University Press, vol. 24(3), pages 726-748, June.
    33. Carratù, Maria & Chiarini, Bruno & Piselli, Paolo, 2020. "Effects of European emission unit allowance auctions on corporate profitability," Energy Policy, Elsevier, vol. 144(C).
    34. 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.
    35. Lee, Jungyoon & Robinson, Peter M., 2015. "Panel nonparametric regression with fixed effects," Journal of Econometrics, Elsevier, vol. 188(2), pages 346-362.
    36. Bolat, C. Kaan & Soytas, Ugur & Akinoglu, Bulent & Nazlioglu, Saban, 2023. "Is there a macroeconomic carbon rebound effect in EU ETS?," Energy Economics, Elsevier, vol. 125(C).
    37. Su, Liangjun & Jin, Sainan, 2012. "Sieve estimation of panel data models with cross section dependence," Journal of Econometrics, Elsevier, vol. 169(1), pages 34-47.
    38. Peter C. B. Phillips & Donggyu Sul, 2003. "Dynamic panel estimation and homogeneity testing under cross section dependence *," Econometrics Journal, Royal Economic Society, vol. 6(1), pages 217-259, June.
    39. Alexandra Soberon & Juan M Rodriguez-Poo & Peter M Robinson, 2022. "Nonparametric panel data regression with parametric cross-sectional dependence," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 114-133.
    40. Costantini, Valeria & Mazzanti, Massimiliano & Montini, Anna, 2013. "Environmental performance, innovation and spillovers. Evidence from a regional NAMEA," Ecological Economics, Elsevier, vol. 89(C), pages 101-114.
    41. repec:hal:journl:peer-00796743 is not listed on IDEAS
    42. Martin Wagner, 2015. "The Environmental Kuznets Curve, Cointegration and Nonlinearity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(6), pages 948-967, September.
    43. Artūras Juodis & Simon Reese, 2022. "The Incidental Parameters Problem in Testing for Remaining Cross-Section Correlation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1191-1203, June.
    44. Robinson, Peter M., 2012. "Nonparametric trending regression with cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 169(1), pages 4-14.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Massimiliano Mazzanti & Antonio Musolesi, 2020. "Modeling Green Knowledge Production and Environmental Policies with Semiparametric Panel Data Regression models," SEEDS Working Papers 1420, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Sep 2020.
    2. Gioldasis, Georgios & Musolesi, Antonio & Simioni, Michel, 2023. "Interactive R&D spillovers: An estimation strategy based on forecasting-driven model selection," International Journal of Forecasting, Elsevier, vol. 39(1), pages 144-169.
    3. 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.
    4. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2021. "Interactive R&D Spillovers: An estimation strategy based on forecasting-driven model selection," SEEDS Working Papers 0621, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Jun 2021.
    5. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2021. "Interactive R&D Spillovers: an estimation strategy based on forecasting-driven model selection," Working Papers hal-03224910, HAL.
    6. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2020. "Model uncertainty, nonlinearities and out-of-sample comparison: evidence from international technology diffusion," Working Papers hal-02790523, HAL.
    7. Musolesi, Antonio & Prete, Giada Andrea & Simioni, Michel, 2022. "Is infrastructure capital really productive? Non-parametric modeling and data-driven model selection in a cross-sectionally dependent panel framework," TSE Working Papers 22-1335, Toulouse School of Economics (TSE).
    8. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2020. "Model uncertainty, nonlinearities and out-of-sample comparison: evidence from international technology diffusion," SEEDS Working Papers 0120, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Jan 2020.
    9. R. Golinelli & I. Mammi & A. Musolesi, 2018. "Parameter heterogeneity, persistence and cross-sectional dependence: new insights on fiscal policy reaction functions for the Euro area," Working Papers wp1120, Dipartimento Scienze Economiche, Universita' di Bologna.
    10. Massimiliano Mazzanti & Antonio Musolesi, 2020. "A Semiparametric Analysis of Green Inventions and Environmental Policies," SEEDS Working Papers 0920, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Jun 2020.
    11. Alexandra Soberon & Antonio Musolesi & Juan M. Rodriguez‐Poo, 2024. "A Semi‐parametric Panel Data Model with Common Factors and Spatial Dependence," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(4), pages 905-927, August.
    12. Antonio Musolesi & Giada Andrea Prete & Michel Simioni, 2022. "Is infrastructure capital really productive? Non-parametric modeling and data-driven model selection in a cross-sectionally dependent panel framework," SEEDS Working Papers 0522, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Mar 2022.
    13. Cem Ertur & Antonio Musolesi, 2017. "Weak and Strong Cross‐Sectional Dependence: A Panel Data Analysis of International Technology Diffusion," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 477-503, April.
    14. Su, Liangjun & Yang, Zhenlin, 2015. "QML estimation of dynamic panel data models with spatial errors," Journal of Econometrics, Elsevier, vol. 185(1), pages 230-258.
    15. Román Mínguez & Roberto Basile & María Durbán, 2020. "An alternative semiparametric model for spatial panel data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(4), pages 669-708, December.
    16. Elhorst, J. Paul & Madre, Jean-Loup & Pirotte, Alain, 2020. "Car traffic, habit persistence, cross-sectional dependence, and spatial heterogeneity: New insights using French departmental data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 614-632.
    17. Cem Ertur & Antonio Musolesi, 2014. "Dépendance individuelle forte et faible : une analyse en données de panel de la diffusion internationale de la technologie," Working Papers halshs-01015208, HAL.
    18. Saptorshee Kanto Chakraborty & Massimiliano Mazzanti, 2021. "Revisiting the literature on the dynamic Environmental Kuznets Curves using a latent structure approach," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 38(3), pages 923-941, October.
    19. Bailey, N. & Ditzen, J. & Holly, S., 2025. "My neighbour's neighbour is not my neighbour: Instrumentation and causality in spatial models," Cambridge Working Papers in Economics 2501, Faculty of Economics, University of Cambridge.
    20. Markus Eberhardt & Francis Teal, 2011. "Econometrics For Grumblers: A New Look At The Literature On Cross‐Country Growth Empirics," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 109-155, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jmvana:v:206:y:2025:i:c:s0047259x24001003. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.