IDEAS home Printed from https://ideas.repec.org/p/zbw/sfb373/200074.html
   My bibliography  Save this paper

Leave-k-out diagnostics in state space models

Author

Listed:
  • Proietti, Tommaso

Abstract

The paper derives an algorithm for computing leave-k-out diagnostics for the detection of patches of outliers for stationary and non-stationary state space models with regression effects. The algorithm is based on a reverse run of the Kalman filter on the smoothing errors and is both efficient and easy to implement. An illustration concerning the US index of industrial production for Textiles proves the effectiveness of multiple deletion diagnostics in unmasking clusters of outlying observations.

Suggested Citation

  • Proietti, Tommaso, 2000. "Leave-k-out diagnostics in state space models," SFB 373 Discussion Papers 2000,74, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  • Handle: RePEc:zbw:sfb373:200074
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/62248/1/723854424.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Atkinson, A. C. & Koopman, S. J. & Shephard, N., 1997. "Detecting shocks: Outliers and breaks in time series," Journal of Econometrics, Elsevier, vol. 80(2), pages 387-422, October.
    2. Sichel, Daniel E, 1993. "Business Cycle Asymmetry: A Deeper Look," Economic Inquiry, Western Economic Association International, vol. 31(2), pages 224-236, April.
    3. Harvey, Andrew C & Koopman, Siem Jan, 1992. "Diagnostic Checking of Unobserved-Components Time Series Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 377-389, October.
    4. John Haslett & Kevin Hayes, 1998. "Residuals for the linear model with general covariance structure," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(1), pages 201-215.
    5. Proietti Tommaso, 1998. "Characterizing Asymmetries in Business Cycles Using Smooth-Transition Structural Time-Series Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 3(3), pages 1-18, 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. Palma, Wilfredo & Bondon, Pascal & Tapia, José, 2008. "Assessing influence in Gaussian long-memory models," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4487-4501, May.
    2. Tommaso Proietti, 2005. "Forecasting and signal extraction with misspecified models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(8), pages 539-556.
    3. Proietti, Tommaso, 2007. "Signal extraction and filtering by linear semiparametric methods," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 935-958, October.
    4. Kevin Boyle & Christopher Parmeter & Brent Boehlert & Robert Paterson, 2013. "Due Diligence in Meta-analyses to Support Benefit Transfers," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 55(3), pages 357-386, July.
    5. Proietti, Tommaso, 2003. "Forecasting the US unemployment rate," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 451-476, March.

    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. Marczak, Martyna & Proietti, Tommaso, 2016. "Outlier detection in structural time series models: The indicator saturation approach," International Journal of Forecasting, Elsevier, vol. 32(1), pages 180-202.
    2. Emilio Zanetti Chini, 2013. "Generalizing smooth transition autoregressions," CREATES Research Papers 2013-32, Department of Economics and Business Economics, Aarhus University.
    3. Proietti, Tommaso, 2003. "Forecasting the US unemployment rate," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 451-476, March.
    4. Monica Billio & Massimiliano Caporin & Guido Cazzavillan, 2008. "Dating EU15 monthly business cycle jointly using GDP and IPI," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2007(3), pages 333-366.
    5. Garcia-Ferrer, Antonio & Queralt, Ricardo & Blazquez, Cristina, 2001. "A growth cycle characterisation and forecasting of the Spanish economy: 1970-1998," International Journal of Forecasting, Elsevier, vol. 17(3), pages 517-532.
    6. Vujić, Sunčica & Commandeur, Jacques J.F. & Koopman, Siem Jan, 2016. "Intervention time series analysis of crime rates: The case of sentence reform in Virginia," Economic Modelling, Elsevier, vol. 57(C), pages 311-323.
    7. Paresh Kumar Narayan & Seema Narayan, 2008. "Examining The Asymmetric Behaviour Of Macroeconomic Aggregates In Asian Economies," Pacific Economic Review, Wiley Blackwell, vol. 13(5), pages 567-574, December.
    8. Dongya Koh & Raül Santaeulàlia-Llopis, 2017. "Countercyclical Elasticity of Substitution," Working Papers 946, Barcelona School of Economics.
    9. Claude DIEBOLT & Jamel TRABELSI, 2009. "Human Capital and French Macroeconomic Growth in the Long Run," Economies et Sociétés (Serie 'Histoire Economique Quantitative'), Association Française de Cliométrie (AFC), issue 40, pages 901-917, May.
    10. Richard H. Clarida & Mark P. Taylor, 2003. "Nonlinear Permanent - Temporary Decompositions in Macroeconomics and Finance," Economic Journal, Royal Economic Society, vol. 113(486), pages 125-139, March.
    11. Chun-Chang Lee & Chih-Min Liang & Hsing-Jung Chou, 2013. "Identifying Taiwan real estate cycle turning points- An application of the multivariate Markov-switching autoregressive Model," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 3(2), pages 1-1.
    12. Toshiya Ishikawa, 2004. "Technology Diffusion and Business Cycle Asymmetry," DEGIT Conference Papers c009_016, DEGIT, Dynamics, Economic Growth, and International Trade.
    13. Daniel M. Chin & John Geweke & Preston J. Miller, 2000. "Predicting turning points," Staff Report 267, Federal Reserve Bank of Minneapolis.
    14. Broto Carmen & Ruiz Esther, 2009. "Testing for Conditional Heteroscedasticity in the Components of Inflation," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(2), pages 1-30, May.
    15. Yi Wen & Huabin Wu, 2011. "Dynamics of externalities: a second-order perspective," Review, Federal Reserve Bank of St. Louis, vol. 93(May), pages 187-206.
    16. Martha Misas & María Teresa Ramírez, 2005. "Depressions In The Colombian Economic Growth During The Xx Century:A Markov Switching Regime Model," Borradores de Economia 2274, Banco de la Republica.
    17. Marian Vavra, 2012. "A Note on the Finite Sample Properties of the CLS Method of TAR Models," Birkbeck Working Papers in Economics and Finance 1206, Birkbeck, Department of Economics, Mathematics & Statistics.
    18. Simon Gilchrist & John C. Williams, 2000. "Putty-Clay and Investment: A Business Cycle Analysis," Journal of Political Economy, University of Chicago Press, vol. 108(5), pages 928-960, October.
    19. Kapetanios, G. & Tzavalis, E., 2010. "Modeling structural breaks in economic relationships using large shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 34(3), pages 417-436, March.
    20. Stéphane Dupraz & Emi Nakamura & Jón Steinsson, 2019. "A Plucking Model of Business Cycles," NBER Working Papers 26351, National Bureau of Economic Research, Inc.

    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:zbw:sfb373:200074. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/sfhubde.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.