IDEAS home Printed from https://ideas.repec.org/a/mup/actaun/actaun_2013061072151.html
   My bibliography  Save this article

Heteroskedasticity, temporal and spatial correlation matter

Author

Listed:
  • Ladislava Grochová

    (Department of Economics, Department of Statistics and Operation Analysis, Mendel University in Brno, 613 00 Brno, Czech Republic)

  • Luboš Střelec

    (Department of Economics, Department of Statistics and Operation Analysis, Mendel University in Brno, 613 00 Brno, Czech Republic)

Abstract

As economic time series or cross sectional data are typically affected by serial correlation and/or heteroskedasticity of unknown form, panel data typically contains some form of heteroskedasticity, serial correlation and/or spatial correlation. Therefore, robust inference in the presence of heteroskedasticity and spatial dependence is an important problem in spatial data analysis. In this paper we study the standard errors based on the HAC of cross-section averages that follows Vogelsang's (2012) fixed-b asymptotic theory, i.e. we continue with Driscoll and Kraay approach (1998). The Monte Carlo simulations are used to investigate the finite sample properties of commonly used estimators both not accounting and accounting for heteroskedasticity and spatiotemporal dependence (OLS, GLS) in comparison to brand new estimator based on Vogelsang's (2012) fixed-b asymptotic theory in the presence of cross-sectional heteroskedasticity and serial and spatial correlation in panel data with fixed effects. Our Monte Carlo experiment shows that the OLS exhibits an important downward bias in all of the cases and almost always has the worst performance when compared to the other estimators. The GLS corrected for HACSC performs well if time dimension is greater than cross-sectional dimension. The best performance can be attributed to the Vogelsang's estimator with fixed-b version of Driscoll-Kraay standard errors.

Suggested Citation

  • Ladislava Grochová & Luboš Střelec, 2013. "Heteroskedasticity, temporal and spatial correlation matter," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 61(7), pages 2151-2155.
    • Handle: RePEc:mup:actaun:actaun_2013061072151
    DOI: 10.11118/actaun201361072151
    as

    Download full text from publisher

    File URL: http://acta.mendelu.cz/doi/10.11118/actaun201361072151.html
    Download Restriction: free of charge
    File URL: http://acta.mendelu.cz/doi/10.11118/actaun201361072151.pdf
    Download Restriction: free of charge
    File URL: https://libkey.io/10.11118/actaun201361072151?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. Kim, Min Seong & Sun, Yixiao, 2013. "Heteroskedasticity and spatiotemporal dependence robust inference for linear panel models with fixed effects," Journal of Econometrics, Elsevier, vol. 177(1), pages 85-108.
    2. Vogelsang, Timothy J., 2012. "Heteroskedasticity, autocorrelation, and spatial correlation robust inference in linear panel models with fixed-effects," Journal of Econometrics, Elsevier, vol. 166(2), pages 303-319.
    3. Conley, Timothy G. & Molinari, Francesca, 2007. "Spatial correlation robust inference with errors in location or distance," Journal of Econometrics, Elsevier, vol. 140(1), pages 76-96, September.
    4. Kelejian, Harry H. & Prucha, Ingmar R., 2007. "HAC estimation in a spatial framework," Journal of Econometrics, Elsevier, vol. 140(1), pages 131-154, September.
    5. Moscone, Francesco & Tosetti, Elisa, 2012. "HAC estimation in spatial panels," Economics Letters, Elsevier, vol. 117(1), pages 60-65.
    6. John C. Driscoll & Aart C. Kraay, 1998. "Consistent Covariance Matrix Estimation With Spatially Dependent Panel Data," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 549-560, November.
    7. Kim, Min Seong & Sun, Yixiao, 2011. "Spatial heteroskedasticity and autocorrelation consistent estimation of covariance matrix," Journal of Econometrics, Elsevier, vol. 160(2), pages 349-371, February.
    8. Hansen, Christian B., 2007. "Asymptotic properties of a robust variance matrix estimator for panel data when T is large," Journal of Econometrics, Elsevier, vol. 141(2), pages 597-620, December.
    9. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    10. Arellano, M, 1987. "Computing Robust Standard Errors for Within-Groups Estimators," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 49(4), pages 431-434, November.
    11. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    12. Conley, T. G., 1999. "GMM estimation with cross sectional dependence," Journal of Econometrics, Elsevier, vol. 92(1), pages 1-45, September.
    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. Kim, Min Seong & Sun, Yixiao, 2013. "Heteroskedasticity and spatiotemporal dependence robust inference for linear panel models with fixed effects," Journal of Econometrics, Elsevier, vol. 177(1), pages 85-108.
    2. Sun, Yu & Yan, Karen X., 2019. "Inference on Difference-in-Differences average treatment effects: A fixed-b approach," Journal of Econometrics, Elsevier, vol. 211(2), pages 560-588.
    3. Gupta, Abhimanyu, 2018. "Autoregressive spatial spectral estimates," Journal of Econometrics, Elsevier, vol. 203(1), pages 80-95.
    4. Vogelsang, Timothy J., 2012. "Heteroskedasticity, autocorrelation, and spatial correlation robust inference in linear panel models with fixed-effects," Journal of Econometrics, Elsevier, vol. 166(2), pages 303-319.
    5. Moscone, F. & Tosetti, Elisa, 2015. "Robust estimation under error cross section dependence," Economics Letters, Elsevier, vol. 133(C), pages 100-104.
    6. Timothy Conley & Silvia Gonçalves & Christian Hansen, 2018. "Inference with Dependent Data in Accounting and Finance Applications," Journal of Accounting Research, Wiley Blackwell, vol. 56(4), pages 1139-1203, September.
    7. Hidalgo, Javier & Schafgans, Marcia, 2021. "Inference without smoothing for large panels with cross-sectional and temporal dependence," Journal of Econometrics, Elsevier, vol. 223(1), pages 125-160.
    8. David Powell, 2017. "Inference with Correlated Clusters," Working Papers WR-1137-1, RAND Corporation.
    9. Hidalgo, Javier & Schafgans, Marcia, 2021. "Inference without smoothing for large panels with cross-sectional and temporal dependence," LSE Research Online Documents on Economics 107426, London School of Economics and Political Science, LSE Library.
    10. Bai, Jushan & Choi, Sung Hoon & Liao, Yuan, 2024. "Standard errors for panel data models with unknown clusters," Journal of Econometrics, Elsevier, vol. 240(2).
    11. Min Seong Kim, 2021. "Robust Inference for Diffusion-Index Forecasts with Cross-Sectionally Dependent Data," Working papers 2021-04, University of Connecticut, Department of Economics.
    12. Moscone, Francesco & Tosetti, Elisa, 2012. "HAC estimation in spatial panels," Economics Letters, Elsevier, vol. 117(1), pages 60-65.
    13. J. Hidalgo & M. Schafgans, 2020. "Inference without smoothing for large panels with cross-sectional and temporal dependence," Papers 2006.14409, arXiv.org.
    14. Zhenhao Gong & Min Seong Kim, 2024. "Improved Inference for Interactive Fixed Effects Model under Cross-Sectional Dependence," Working papers 2024-02, University of Connecticut, Department of Economics.
    15. Pakel, Cavit, 2019. "Bias reduction in nonlinear and dynamic panels in the presence of cross-section dependence," Journal of Econometrics, Elsevier, vol. 213(2), pages 459-492.
    16. Jushan Bai & Sung Hoon Choi & Yuan Liao, 2021. "Feasible generalized least squares for panel data with cross-sectional and serial correlations," Empirical Economics, Springer, vol. 60(1), pages 309-326, January.
    17. Kojevnikov, Denis & Marmer, Vadim & Song, Kyungchul, 2021. "Limit theorems for network dependent random variables," Journal of Econometrics, Elsevier, vol. 222(2), pages 882-908.
    18. Hoechle, Daniel & Schmid, Markus & Zimmermann, Heinz, 2012. "Decomposing Performance," Working Papers on Finance 1216, University of St. Gallen, School of Finance, revised Nov 2015.
    19. Zhenhao Gong & Min Seong Kim, 2024. "Improved inference for interactive fixed effects model under cross-sectional dependence," Empirical Economics, Springer, vol. 67(2), pages 727-760, August.
    20. Kim, Min Seong & Sun, Yixiao, 2011. "Spatial heteroskedasticity and autocorrelation consistent estimation of covariance matrix," Journal of Econometrics, Elsevier, vol. 160(2), pages 349-371, 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:mup:actaun:actaun_2013061072151. 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: Ivo Andrle (email available below). General contact details of provider: https://mendelu.cz/en/ .

    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.