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Leave-K-Out Diagnostics In State-Space Models

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  • Tommaso Proietti

Abstract

The paper derives an algorithm for computing leave-k-out diagnostics for the detection of patches of outliers for stationary and nonstationary 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. The US index of industrial production for textiles is used to illustrate the application of the algorithm. Copyright 2003 Blackwell Publishing Ltd.

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  • Tommaso Proietti, 2003. "Leave-K-Out Diagnostics In State-Space Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(2), pages 221-236, March.
  • Handle: RePEc:bla:jtsera:v:24:y:2003:i:2:p:221-236
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    References listed on IDEAS

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    1. Sichel, Daniel E, 1993. "Business Cycle Asymmetry: A Deeper Look," Economic Inquiry, Western Economic Association International, vol. 31(2), pages 224-236, April.
    2. 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.
    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.
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    Cited by:

    1. Proietti, Tommaso, 2007. "Signal extraction and filtering by linear semiparametric methods," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 935-958, October.
    2. 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.
    3. Tommaso Proietti, 2005. "Forecasting and signal extraction with misspecified models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(8), pages 539-556.
    4. 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.

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