IDEAS home Printed from https://ideas.repec.org/a/bla/jtsera/v25y2004i6p831-872.html
   My bibliography  Save this article

Nonparametric Estimation and Testing in Panels of Intercorrelated Time Series

Author

Listed:
  • Vidar Hjellvik
  • Rong Chen
  • Dag Tjøstheim

Abstract

. We consider nonparametric estimation and testing of linearity in a panel of intercorrelated time series. We place the emphasis on the situation where there are many time series in the panel but few observations for each of the series. The intercorrelation is described by a latent process, and a conditioning argument involving this process plays an important role in deriving the asymptotic theory. To be accurate the asymptotic distribution of the test functional of linearity requires a very large number of observations, and bootstrapping gives much better finite sample results. A number of simulation experiments and an illustration on a real data set are included.

Suggested Citation

  • Vidar Hjellvik & Rong Chen & Dag Tjøstheim, 2004. "Nonparametric Estimation and Testing in Panels of Intercorrelated Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(6), pages 831-872, November.
    • Handle: RePEc:bla:jtsera:v:25:y:2004:i:6:p:831-872
    DOI: 10.1111/j.1467-9892.2004.00382.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9892.2004.00382.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9892.2004.00382.x?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
    ---><---

    References listed on IDEAS

    as
    1. Forni, Mario, et al, 2001. "Coincident and Leading Indicators for the Euro Area," Economic Journal, Royal Economic Society, vol. 111(471), pages 62-85, May.
    2. Holger Dette & Ingrid Spreckelsen, 2004. "Some comments on specification tests in nonparametric absolutely regular processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(2), pages 159-172, March.
    3. Chamberlain, Gary & Rothschild, Michael, 1983. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Econometrica, Econometric Society, vol. 51(5), pages 1281-1304, September.
    4. Forni, Mario & Lippi, Marco, 2001. "The Generalized Dynamic Factor Model: Representation Theory," Econometric Theory, Cambridge University Press, vol. 17(6), pages 1113-1141, December.
    5. Cai, Zongwu & Fan, Jianqing & Yao, Qiwei, 2000. "Functional-coefficient regression models for nonlinear time series," LSE Research Online Documents on Economics 6314, London School of Economics and Political Science, LSE Library.
    6. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    7. Chamberlain, Gary, 1983. "Funds, Factors, and Diversification in Arbitrage Pricing Models," Econometrica, Econometric Society, vol. 51(5), pages 1305-1323, September.
    8. Shiqing Ling, 2004. "Estimation and testing stationarity for double‐autoregressive models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 63-78, February.
    9. Arellano, Manuel, 2003. "Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780199245291.
    10. Hjellvik, V. & Yao, Q. & Tjostheim, D., 1996. "Linearity Testing using Local Polynomial Approximation," SFB 373 Discussion Papers 1996,60, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    11. Hjellvik, Vidar & Yao, Qiwei & Tjostheim, Dag, 1998. "Linearity testing using local polynominal approximation," LSE Research Online Documents on Economics 6638, London School of Economics and Political Science, LSE Library.
    12. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    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. Isabel Casas & Jiti Gao & Shangyu Xie, 2018. "Modelling time-varying income elasticities of health care expenditure for the OECD," Monash Econometrics and Business Statistics Working Papers 22/18, Monash University, Department of Econometrics and Business Statistics.
    2. Hidalgo, Javier & Schafgans, Marcia, 2017. "Inference and testing breaks in large dynamic panels with strong cross sectional dependence," Journal of Econometrics, Elsevier, vol. 196(2), pages 259-274.
    3. Jia Chen & Jiti Gao, 2014. "Semiparametric Model Selection in Panel Data Models with Deterministic Trends and Cross-Sectional Dependence," Monash Econometrics and Business Statistics Working Papers 15/14, Monash University, Department of Econometrics and Business Statistics.
    4. Javier Hidalgo & Marcia M Schafgans, 2015. "Inference and Testing Breaks in Large Dynamic Panels with Strong Cross Sectional Dependence," STICERD - Econometrics Paper Series /2015/583, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    5. Jia Chen & Jiti Gao & Degui Li, 2013. "Estimation in Single-Index Panel Data Models with Heterogeneous Link Functions," Econometric Reviews, Taylor & Francis Journals, vol. 32(8), pages 928-955, November.
    6. He, Bang-Qiang & Hong, Xing-Jian & Fan, Guo-Liang, 2017. "Block empirical likelihood for partially linear panel data models with fixed effects," Statistics & Probability Letters, Elsevier, vol. 123(C), pages 128-138.
    7. Jia Chen & Jiti Gao & Degui Li, 2013. "Estimation in Partially Linear Single-Index Panel Data Models With Fixed Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 315-330, July.
    8. Jia Chen & Degui Li & Jiti Gao, 2013. "Non- and Semi-Parametric Panel Data Models: A Selective Review," Monash Econometrics and Business Statistics Working Papers 18/13, Monash University, Department of Econometrics and Business Statistics.
    9. Chen, Jia & Li, Degui & Xia, Yingcun, 2019. "Estimation of a rank-reduced functional-coefficient panel data model with serial correlation," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 456-479.
    10. Liping Zhu & Jinhong You & Qunfang Xu, 2014. "Statistical Inference for Single-index Panel Data Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(3), pages 830-843, September.
    11. Hidalgo, Javier & Schafgans, Marcia, 2017. "Inference and testing breaks in large dynamic panels with strong cross sectional dependence," LSE Research Online Documents on Economics 68839, London School of Economics and Political Science, LSE Library.

    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. Hallin, Marc & Lippi, Marco, 2013. "Factor models in high-dimensional time series—A time-domain approach," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2678-2695.
    2. Tomohiro Ando & Jushan Bai, 2016. "Panel Data Models with Grouped Factor Structure Under Unknown Group Membership," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 163-191, January.
    3. Forni, Mario & Gambetti, Luca, 2010. "The dynamic effects of monetary policy: A structural factor model approach," Journal of Monetary Economics, Elsevier, vol. 57(2), pages 203-216, March.
    4. Gupta, Rangan & Kabundi, Alain, 2011. "A large factor model for forecasting macroeconomic variables in South Africa," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1076-1088, October.
    5. Philipp Gersing & Matteo Barigozzi & Christoph Rust & Manfred Deistler, 2023. "The Canonical Decomposition of Factor Models: Weak Factors are Everywhere," Papers 2307.10067, arXiv.org, revised Feb 2025.
    6. Marc Hallin, 2022. "Manfred Deistler and the General Dynamic Factor Model Approach to the Analysis of High-Dimensional Time Series," Working Papers ECARES 2022-30, ULB -- Universite Libre de Bruxelles.
    7. Reichlin, Lucrezia, 2002. "Factor Models in Large Cross-Sections of Time Series," CEPR Discussion Papers 3285, C.E.P.R. Discussion Papers.
    8. Forni, Mario & Hallin, Marc & Lippi, Marco & Zaffaroni, Paolo, 2015. "Dynamic factor models with infinite-dimensional factor spaces: One-sided representations," Journal of Econometrics, Elsevier, vol. 185(2), pages 359-371.
    9. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    10. Hallin, Marc & Liska, Roman, 2011. "Dynamic factors in the presence of blocks," Journal of Econometrics, Elsevier, vol. 163(1), pages 29-41, July.
    11. Lippi, Marco & Deistler, Manfred & Anderson, Brian, 2023. "High-Dimensional Dynamic Factor Models: A Selective Survey and Lines of Future Research," Econometrics and Statistics, Elsevier, vol. 26(C), pages 3-16.
    12. Lippi, Marco & Reichlin, Lucrezia & Forni, Mario, 2003. "Opening the Black Box: Structural Factor Models versus Structural VARs," CEPR Discussion Papers 4133, C.E.P.R. Discussion Papers.
    13. Massacci, Daniele, 2017. "Least squares estimation of large dimensional threshold factor models," Journal of Econometrics, Elsevier, vol. 197(1), pages 101-129.
    14. Alexander Chudik & Roland Straub, 2017. "Size, Openness, And Macroeconomic Interdependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 58(1), pages 33-55, February.
    15. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Papers 2310.17278, arXiv.org, revised Jan 2024.
    16. Barigozzi, Matteo & Trapani, Lorenzo, 2020. "Sequential testing for structural stability in approximate factor models," Stochastic Processes and their Applications, Elsevier, vol. 130(8), pages 5149-5187.
    17. Alhassan Abdullah Mohammed, 2011. "A Coincident Indicator of the Gulf Cooperation Council Business Cycle," Review of Middle East Economics and Finance, De Gruyter, vol. 6(3), pages 1-23, February.
    18. Angelopoulos, Jason & Sahoo, Satya & Visvikis, Ilias D., 2020. "Commodity and transportation economic market interactions revisited: New evidence from a dynamic factor model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    19. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
    20. Bai, Jushan & Liao, Yuan, 2016. "Efficient estimation of approximate factor models via penalized maximum likelihood," Journal of Econometrics, Elsevier, vol. 191(1), pages 1-18.

    More about this item

    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:bla:jtsera:v:25:y:2004:i:6:p:831-872. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0143-9782 .

    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.