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Leave-k-out diagnostics in state space models

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  • 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. --

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Bibliographic Info

Paper provided by Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes in its series SFB 373 Discussion Papers with number 2000,74.

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Date of creation: 2000
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Handle: RePEc:zbw:sfb373:200074

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Keywords: Kalman filter and smoother; influence; outliers; structural time series models;

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  1. Sichel, D.E., 1988. "Business Cycle Asymmetry: A Deeper Look," Papers, Princeton, Department of Economics - Financial Research Center 85, Princeton, Department of Economics - Financial Research Center.
  2. Proietti Tommaso, 1998. "Characterizing Asymmetries in Business Cycles Using Smooth-Transition Structural Time-Series Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, 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, American Statistical Association, vol. 10(4), pages 377-89, October.
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Cited by:
  1. Proietti, Tommaso, 2007. "Signal extraction and filtering by linear semiparametric methods," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 52(2), pages 935-958, October.
  2. Tommaso Proietti, 2005. "Forecasting and signal extraction with misspecified models," Journal of Forecasting, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 24(8), pages 539-556.
  3. Palma, Wilfredo & Bondon, Pascal & Tapia, José, 2008. "Assessing influence in Gaussian long-memory models," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 52(9), pages 4487-4501, May.
  4. Kevin Boyle & Christopher Parmeter & Brent Boehlert & Robert Paterson, 2013. "Due Diligence in Meta-analyses to Support Benefit Transfers," Environmental & Resource Economics, European Association of Environmental and Resource Economists, European Association of Environmental and Resource Economists, vol. 55(3), pages 357-386, July.

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