IDEAS home Printed from https://ideas.repec.org/a/bpj/jecome/v9y2020i1p22n3.html
   My bibliography  Save this article

Instrumental Variables Estimation in Large Heterogeneous Panels with Multifactor Structure

Author

Listed:
  • Forchini Giovanni

    (Department of Economics, Umeå University, Umea, Sweden)

  • Jiang Bin

    (Department of Econometrics and Business Statistics, Monash University, Australia)

  • Peng Bin

    (Department of Economics, University of Bath, UK)

Abstract

The paper proposes new instrumental variables estimators for the slope parameters of a panel data model with classical endogeneity in which all the observables – including the instruments – may have a common factors structure. These estimators are shown to be consistent and asymptotically normal under weak regularity conditions. A small Monte Carlo simulation shows that these estimators compare favourably to existing estimators.

Suggested Citation

  • Forchini Giovanni & Jiang Bin & Peng Bin, 2020. "Instrumental Variables Estimation in Large Heterogeneous Panels with Multifactor Structure," Journal of Econometric Methods, De Gruyter, vol. 9(1), pages 1-22, January.
    • Handle: RePEc:bpj:jecome:v:9:y:2020:i:1:p:22:n:3
    DOI: 10.1515/jem-2018-0003
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jem-2018-0003
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/jem-2018-0003?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. 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.
    2. Alonso-Borrego, Cesar & Arellano, Manuel, 1999. "Symmetrically Normalized Instrumental-Variable Estimation Using Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 36-49, January.
    3. Bloom, Nick & Griffith, Rachel & Van Reenen, John, 2002. "Do R&D tax credits work? Evidence from a panel of countries 1979-1997," Journal of Public Economics, Elsevier, vol. 85(1), pages 1-31, July.
    4. Arellano, Manuel, 2016. "Modelling optimal instrumental variables for dynamic panel data models," Research in Economics, Elsevier, vol. 70(2), pages 238-261.
    5. Wooldridge, Jeffrey M., 2005. "Instrumental Variables Estimation With Panel Data," Econometric Theory, Cambridge University Press, vol. 21(4), pages 865-869, August.
    6. Arouri, Mohamed El Hedi & Ben Youssef, Adel & M'henni, Hatem & Rault, Christophe, 2012. "Energy consumption, economic growth and CO2 emissions in Middle East and North African countries," Energy Policy, Elsevier, vol. 45(C), pages 342-349.
    7. Baltagi, Badi H. & Moscone, Francesco, 2010. "Health care expenditure and income in the OECD reconsidered: Evidence from panel data," Economic Modelling, Elsevier, vol. 27(4), pages 804-811, July.
    8. Harding, Matthew & Lamarche, Carlos, 2014. "Estimating and testing a quantile regression model with interactive effects," Journal of Econometrics, Elsevier, vol. 178(P1), pages 101-113.
    9. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
    10. Giovanni Forchini & Bin Jiang, 2019. "The unconditional distributions of the OLS, TSLS and LIML estimators in a simple structural equations model," Econometric Reviews, Taylor & Francis Journals, vol. 38(2), pages 208-247, February.
    11. Harding, Matthew & Lamarche, Carlos, 2011. "Least squares estimation of a panel data model with multifactor error structure and endogenous covariates," Economics Letters, Elsevier, vol. 111(3), pages 197-199, June.
    12. Kelejian, Harry H, 1974. "Random Parameters in a Simultaneous Equation Framework: Identification and Estimation," Econometrica, Econometric Society, vol. 42(3), pages 517-527, May.
    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. Giovanni Forchini & Bin Jiang & Bin Peng, 2018. "TSLS and LIML Estimators in Panels with Unobserved Shocks," Econometrics, MDPI, vol. 6(2), pages 1-12, April.
    2. G. Forchini & Bin Jiang & Bin Peng, 2015. "Consistent Estimation in Large Heterogeneous Panels with Multifactor Structure Endogeneity," Monash Econometrics and Business Statistics Working Papers 14/15, Monash University, Department of Econometrics and Business Statistics.
    3. Giovanni Forchini & Bin Jiang & Bin Peng, 2015. "Consistent Estimation in Large Heterogeneous Panels with Multifactor Structure and Endogeneity," School of Economics Discussion Papers 0315, School of Economics, University of Surrey.
    4. Yongfu Huang, 2011. "Private investment and financial development in a globalized world," Empirical Economics, Springer, vol. 41(1), pages 43-56, August.
    5. Ngoc Thien Anh Pham & Nicholas Sim, 2020. "Shipping cost and development of the landlocked developing countries: Panel evidence from the common correlated effects approach," The World Economy, Wiley Blackwell, vol. 43(4), pages 892-920, April.
    6. Fei Liu & Jiti Gao & Yanrong Yang, 2020. "Time-Varying Panel Data Models with an Additive Factor Structure," Monash Econometrics and Business Statistics Working Papers 42/20, Monash University, Department of Econometrics and Business Statistics.
    7. Temple, Jonathan & Van de Sijpe, Nicolas, 2017. "Foreign aid and domestic absorption," Journal of International Economics, Elsevier, vol. 108(C), pages 431-443.
    8. Boneva, Lena & Linton, Oliver, 2017. "A discrete choice model for large heterogeneous panels with interactive fixed effects with an application to the determinants of corporate bond issuance," Bank of England working papers 640, Bank of England.
    9. Andres Sagner, 2020. "High Dimensional Quantile Factor Analysis," Working Papers Central Bank of Chile 886, Central Bank of Chile.
    10. Lena Boneva (Körber) & Oliver Linton, 2017. "A discrete choice model for large heterogeneous panels with interactive fixed effects with an application to the determinants of corporate bond issuance," CeMMAP working papers 02/17, Institute for Fiscal Studies.
    11. Vasilis Sarafidis & Tom Wansbeek, 2012. "Cross-Sectional Dependence in Panel Data Analysis," Econometric Reviews, Taylor & Francis Journals, vol. 31(5), pages 483-531, September.
    12. Artūras Juodis & Yiannis Karavias & Vasilis Sarafidis, 2021. "A homogeneous approach to testing for Granger non-causality in heterogeneous panels," Empirical Economics, Springer, vol. 60(1), pages 93-112, January.
    13. Milda Norkuté & Vasilis Sarafidis & Takashi Yamagata, 2018. "Instrumental Variable Estimation of Dynamic Linear Panel Data Models with Defactored Regressors and a Multifactor Error Structure," ISER Discussion Paper 1019, Institute of Social and Economic Research, Osaka University.
    14. Ye, Xiaoqing & Xu, Juan & Wu, Xiangjun, 2018. "Estimation of an unbalanced panel data Tobit model with interactive effects," Journal of choice modelling, Elsevier, vol. 28(C), pages 108-123.
    15. Isabel Casas & Jiti Gao & Bin Peng & Shangyu Xie, 2021. "Time‐varying income elasticities of healthcare expenditure for the OECD and Eurozone," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(3), pages 328-345, April.
    16. Mohitosh Kejriwal & Xiaoxiao Li & Evan Totty, 2020. "Multidimensional skills and the returns to schooling: Evidence from an interactive fixed‐effects approach and a linked survey‐administrative data set," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 548-566, August.
    17. Hugo Freeman & Martin Weidner, 2021. "Linear panel regressions with two-way unobserved heterogeneity," CeMMAP working papers CWP39/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    18. 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.
    19. Bai, Jushan & Liao, Yuan, 2017. "Inferences in panel data with interactive effects using large covariance matrices," Journal of Econometrics, Elsevier, vol. 200(1), pages 59-78.
    20. Moon, Hyungsik Roger & Weidner, Martin, 2017. "Dynamic Linear Panel Regression Models With Interactive Fixed Effects," Econometric Theory, Cambridge University Press, vol. 33(1), pages 158-195, 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:bpj:jecome:v:9:y:2020:i:1:p:22:n:3. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.