IDEAS home Printed from https://ideas.repec.org/p/ptu/wpaper/w201003.html
   My bibliography  Save this paper

Nonstationary Extremes and the US Business Cycle

Author

Listed:
  • António Rua
  • Miguel de Carvalho

Abstract

Considerable attention has been devoted to the statistical analysis of extreme events. Classical peaks over threshold methods are a popular modelling strategy for extreme value statistics of stationary data. For nonstationary series a variant of the peaks over threshold analysis is routinely applied using covariates as a means to overcome the lack of stationarity in the series of interest. In this paper we concern ourselves with extremes of possibly nonstationary processes. Given that our approach is, in some way, linked to the celebrated Box-Jenkins method, we refer to the procedure proposed and applied herein as Box-Jenkins-Pareto. Our procedure is particularly appropriate for settings where the parameter covariate model is non-trivial or when well qualified covariates are simply unavailable. We apply the Box-Jenkins-Pareto approach to the weekly number of unemployment insurance claims in the US and exploit the connection between threshold exceedances and the US business cycle.

Suggested Citation

  • António Rua & Miguel de Carvalho, 2010. "Nonstationary Extremes and the US Business Cycle," Working Papers w201003, Banco de Portugal, Economics and Research Department.
    • Handle: RePEc:ptu:wpaper:w201003
    as

    Download full text from publisher

    File URL: https://www.bportugal.pt/sites/default/files/anexos/papers/wp201003.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. V. Chavez‐Demoulin & A. C. Davison, 2005. "Generalized additive modelling of sample extremes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(1), pages 207-222, January.
    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. Yun Feng & Weijie Hou & Yuping Song, 2024. "Tail risk forecasting and its application to margin requirements in the commodity futures market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1513-1529, August.
    2. E. Zanini & E. Eastoe & M. J. Jones & D. Randell & P. Jonathan, 2020. "Flexible covariate representations for extremes," Environmetrics, John Wiley & Sons, Ltd., vol. 31(5), August.
    3. Ahmad Aboubacrène Ag & Deme El Hadji & Diop Aliou & Girard Stéphane, 2019. "Estimation of the tail-index in a conditional location-scale family of heavy-tailed distributions," Dependence Modeling, De Gruyter, vol. 7(1), pages 394-417, January.
    4. Julien Hambuckers & Marie Kratz & Antoine Usseglio-Carleve, 2023. "Efficient Estimation In Extreme Value Regression Models Of Hedge Fund Tail Risks," Working Papers hal-04090916, HAL.
    5. Daniela Castro Camilo & Miguel de Carvalho & Jennifer Wadsworth, 2017. "Time-Varying Extreme Value Dependence with Application to Leading European Stock Markets," Papers 1709.01198, arXiv.org.
    6. Christoph Marty & Juliette Blanchet, 2012. "Long-term changes in annual maximum snow depth and snowfall in Switzerland based on extreme value statistics," Climatic Change, Springer, vol. 111(3), pages 705-721, April.
    7. Mhalla, Linda & Chavez-Demoulin, Valérie & Naveau, Philippe, 2017. "Non-linear models for extremal dependence," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 49-66.
    8. Setareh Ranjbar & Eva Cantoni & Valérie Chavez‐Demoulin & Giampiero Marra & Rosalba Radice & Katia Jaton, 2022. "Modelling the extremes of seasonal viruses and hospital congestion: The example of flu in a Swiss hospital," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(4), pages 884-905, August.
    9. Daouia, Abdelaati & Gardes, Laurent & Girard, Stephane, 2011. "On kernel smoothing for extremal quantile regression," LIDAM Discussion Papers ISBA 2011031, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    10. Hongyu An & Boping Tian, 2024. "Varying Index Coefficient Model for Tail Index Regression," Mathematics, MDPI, vol. 12(13), pages 1-35, June.
    11. Bousebata, Meryem & Enjolras, Geoffroy & Girard, Stéphane, 2023. "Extreme partial least-squares," Journal of Multivariate Analysis, Elsevier, vol. 194(C).
    12. Francesca Biagini & Tobias Huber & Johannes G. Jaspersen & Andrea Mazzon, 2021. "Estimating extreme cancellation rates in life insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(4), pages 971-1000, December.
    13. Laurini, Fabrizio & Pauli, Francesco, 2009. "Smoothing sample extremes: The mixed model approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3842-3854, September.
    14. Gardes, Laurent & Girard, Stéphane & Lekina, Alexandre, 2010. "Functional nonparametric estimation of conditional extreme quantiles," Journal of Multivariate Analysis, Elsevier, vol. 101(2), pages 419-433, February.
    15. Abdelaati Daouia & Laurent Gardes & Stéphane Girard & Alexandre Lekina, 2011. "Kernel estimators of extreme level curves," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 311-333, August.
    16. Tong Siu Tung Wong & Wai Keung Li, 2015. "Extreme values identification in regression using a peaks-over-threshold approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(3), pages 566-576, March.
    17. James W. Taylor & Keming Yu, 2016. "Using auto-regressive logit models to forecast the exceedance probability for financial risk management," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(4), pages 1069-1092, October.
    18. Rémillard, Bruno & Nasri, Bouchra & Bouezmarni, Taoufik, 2017. "On copula-based conditional quantile estimators," Statistics & Probability Letters, Elsevier, vol. 128(C), pages 14-20.
    19. Emma F. Eastoe & Jonathan A. Tawn, 2009. "Modelling non‐stationary extremes with application to surface level ozone," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(1), pages 25-45, February.
    20. Padoan, S.A. & Wand, M.P., 2008. "Mixed model-based additive models for sample extremes," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 2850-2858, December.

    More about this item

    JEL classification:

    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    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:ptu:wpaper:w201003. 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: DEE-NTD (email available below). General contact details of provider: https://edirc.repec.org/data/bdpgvpt.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.