IDEAS home Printed from https://ideas.repec.org/p/msh/ebswps/2015-17.html
   My bibliography  Save this paper

Cross-sectional Independence Test for a Class of Parametric Panel Data Models

Author

Listed:
  • Guangming Pan
  • Jiti Gao
  • Yanrong Yang
  • Meihui Guo

Abstract

This paper proposes a new statistic to conduct cross-sectional independence test for the residuals involved in a parametric panel data model. The proposed test statistic, which is called linear spectral statistic (LSS), is established based on the characteristic function of the empirical spectral distribution (ESD) of the sample correlation matrix of the residuals. The main advantage of the proposed test statistic is that it can capture nonlinear cross-sectional dependence. Asymptotic theory for a general class of linear spectral statistics is established, as the cross-sectional dimension N and time length T go to infinity proportionally. This type of statistics covers many classical statistics, including the bias-corrected Lagrange Multiplier (LM) test statistic and the likelihood ratio test statistic. Furthermore, the power under a local alternative hypothesis is analyzed and the asymptotic distribution of the proposed statistic under this local hypothesis is also established. Finite sample performance shows that the proposed test statistic works well numerically in each individual case and it can also distinguish some dependent but uncorrelated structures, for example, nonlinear MA(1) models and multiple ARCH(1) models.

Suggested Citation

  • Guangming Pan & Jiti Gao & Yanrong Yang & Meihui Guo, 2015. "Cross-sectional Independence Test for a Class of Parametric Panel Data Models," Monash Econometrics and Business Statistics Working Papers 17/15, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2015-17
    as

    Download full text from publisher

    File URL: https://www.monash.edu/__data/assets/pdf_file/0009/925875/wp17-15.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Baltagi, Badi H. & Feng, Qu & Kao, Chihwa, 2012. "A Lagrange Multiplier test for cross-sectional dependence in a fixed effects panel data model," Journal of Econometrics, Elsevier, vol. 170(1), pages 164-177.
    2. Hsiao, Cheng & Pesaran, M. Hashem & Pick, Andreas, 2007. "Diagnostic Tests of Cross Section Independence for Nonlinear Panel Data Models," IZA Discussion Papers 2756, Institute of Labor Economics (IZA).
    3. T. S. Breusch & A. R. Pagan, 1980. "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 239-253.
    4. M. Hashem Pesaran & Aman Ullah & Takashi Yamagata, 2008. "A bias-adjusted LM test of error cross-section independence," Econometrics Journal, Royal Economic Society, vol. 11(1), pages 105-127, March.
    5. M. Hashem Pesaran, 2021. "General diagnostic tests for cross-sectional dependence in panels," Empirical Economics, Springer, vol. 60(1), pages 13-50, January.
    6. James R. Schott, 2005. "Testing for complete independence in high dimensions," Biometrika, Biometrika Trust, vol. 92(4), pages 951-956, December.
    7. Hsiao, Cheng & Pesaran, M. Hashem & Pick, Andreas, 2007. "Diagnostic Tests of Cross Section Independence for Nonlinear Panel Data Models," IZA Discussion Papers 2756, Institute of Labor Economics (IZA).
    8. Dozier, R. Brent & Silverstein, Jack W., 2007. "On the empirical distribution of eigenvalues of large dimensional information-plus-noise-type matrices," Journal of Multivariate Analysis, Elsevier, vol. 98(4), pages 678-694, April.
    9. Kuan Chung-Ming & Lee Wei-Ming, 2004. "A New Test of the Martingale Difference Hypothesis," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(4), pages 1-26, December.
    10. Silverstein, J. W., 1995. "Strong Convergence of the Empirical Distribution of Eigenvalues of Large Dimensional Random Matrices," Journal of Multivariate Analysis, Elsevier, vol. 55(2), pages 331-339, November.
    11. Jiti Gao & Xiao Han & Guangming Pan & Yanrong Yang, 2014. "High Dimensional Correlation Matrices: CLT and Its Applications," Monash Econometrics and Business Statistics Working Papers 26/14, Monash University, Department of Econometrics and Business Statistics.
    12. Su, Liangjun & Ullah, Aman, 2009. "Testing Conditional Uncorrelatedness," Journal of Business & Economic Statistics, American Statistical Association, vol. 27, pages 18-29.
    13. Chen, Jia & Gao, Jiti & Li, Degui, 2012. "A New Diagnostic Test For Cross-Section Uncorrelatedness In Nonparametric Panel Data Models," Econometric Theory, Cambridge University Press, vol. 28(5), pages 1144-1163, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dogan, Eyup & Altinoz, Buket & Madaleno, Mara & Taskin, Dilvin, 2020. "The impact of renewable energy consumption to economic growth: A replication and extension of Inglesi-Lotz (2016)," Energy Economics, Elsevier, vol. 90(C).

    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. G. Pan & J. Gao & Y. Yang & M. Guo, 2012. "Independence Test for High Dimensional Random Vectors," Monash Econometrics and Business Statistics Working Papers 1/12, Monash University, Department of Econometrics and Business Statistics.
    2. Zhaoyuan Li & Jianfeng Yao, 2021. "Extension of the Lagrange multiplier test for error cross-section independence to large panels with non normal errors," Papers 2103.06075, arXiv.org.
    3. Usman, Muhammad & Makhdum, Muhammad Sohail Amjad, 2021. "What abates ecological footprint in BRICS-T region? Exploring the influence of renewable energy, non-renewable energy, agriculture, forest area and financial development," Renewable Energy, Elsevier, vol. 179(C), pages 12-28.
    4. Baltagi, Badi H. & Feng, Qu & Kao, Chihwa, 2016. "Estimation of heterogeneous panels with structural breaks," Journal of Econometrics, Elsevier, vol. 191(1), pages 176-195.
    5. Vasilis Sarafidis & Tom Wansbeek, 2012. "Cross-Sectional Dependence in Panel Data Analysis," Econometric Reviews, Taylor & Francis Journals, vol. 31(5), pages 483-531, September.
    6. Alexander Chudik & M. Hashem Pesaran, 2013. "Large panel data models with cross-sectional dependence: a survey," Globalization Institute Working Papers 153, Federal Reserve Bank of Dallas.
    7. Feng, Long & Zhao, Ping & Ding, Yanling & Liu, Binghui, 2021. "Rank-based tests of cross-sectional dependence in panel data models," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
    8. Leyla Mammadova & Aytan Mammadova & Fuad Mammadov & Leyla Yusifzada, 2016. "Determinants of Depositors’ Behaviour: Heterogeneous Panel Estimates," Working Papers 1603, Central Bank of Azerbaijan Republic.
    9. M. Hashem Pesaran, 2021. "General diagnostic tests for cross-sectional dependence in panels," Empirical Economics, Springer, vol. 60(1), pages 13-50, January.
    10. Badi H. Baltagi & Chihwa Kao & Fa Wang, 2017. "Asymptotic power of the sphericity test under weak and strong factors in a fixed effects panel data model," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 853-882, October.
    11. Syed Jawad Hussain Shahzad & Safwan Mohd Nor & Nur Azura Sanusi & Ronald Ravinesh Kumar, 2018. "The Determinants of Credit Risk: Analysis of US Industry-level Indices," Global Business Review, International Management Institute, vol. 19(5), pages 1152-1165, October.
    12. Liu, Xia, 2023. "Tourism development, environmental regulations, and natural resource management: Evidence from G20 countries," Resources Policy, Elsevier, vol. 86(PA).
    13. Lu, Yin & Tian, Tian & Ge, Chen, 2023. "Asymmetric effects of renewable energy, fintech development, natural resources, and environmental regulations on the climate change in the post-covid era," Resources Policy, Elsevier, vol. 85(PB).
    14. Michael Appiah & Bright Akwasi Gyamfi & Tomiwa Sunday Adebayo & Festus Victor Bekun, 2023. "Do financial development, foreign direct investment, and economic growth enhance industrial development? Fresh evidence from Sub-Sahara African countries," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 22(2), pages 203-227, May.
    15. Baltagi, Badi H. & Feng, Qu & Kao, Chihwa, 2012. "A Lagrange Multiplier test for cross-sectional dependence in a fixed effects panel data model," Journal of Econometrics, Elsevier, vol. 170(1), pages 164-177.
    16. Halunga, Andreea G. & Orme, Chris D. & Yamagata, Takashi, 2017. "A heteroskedasticity robust Breusch–Pagan test for Contemporaneous correlation in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 198(2), pages 209-230.
    17. Mahmut Unsal Sasmaz & Emre Sakar & Yunus Emre Yayla & Ulas Akkucuk, 2020. "The Relationship between Renewable Energy and Human Development in OECD Countries: A Panel Data Analysis," Sustainability, MDPI, vol. 12(18), pages 1-16, September.
    18. Mahmut Unsal Sasmaz & Aysun Karamıklı & Ulas Akkucuk, 2021. "The relationship between renewable energy use and health expenditures in EU countries," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(7), pages 1129-1139, September.
    19. Gorus, Muhammed Sehid & Aslan, Murat, 2019. "Impacts of economic indicators on environmental degradation: Evidence from MENA countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 259-268.
    20. Demetrescu, Matei & Hosseinkouchack, Mehdi & Rodrigues, Paulo M. M., 2023. "Tests of no cross-sectional error dependence in panel quantile regressions," Ruhr Economic Papers 1041, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

    More about this item

    Keywords

    Characteristic function; cross–sectional independence; empirical spectral distribution; linear panel data models; Marcenko-Pastur Law;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:msh:ebswps:2015-17. 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: Professor Xibin Zhang (email available below). General contact details of provider: https://edirc.repec.org/data/dxmonau.html .

    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.