Leave-k-out diagnostics in state space models
AbstractThe 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. --
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper 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.
Date of creation: 2000
Date of revision:
Kalman filter and smoother; influence; outliers; structural time series models;
Other versions of this item:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Sichel, Daniel E, 1993. "Business Cycle Asymmetry: A Deeper Look," Economic Inquiry, Western Economic Association International, Western Economic Association International, vol. 31(2), pages 224-36, April.
- Daniel E. Sichel, 1989. "Business cycle asymmetry: a deeper look," Working Paper Series / Economic Activity Section, Board of Governors of the Federal Reserve System (U.S.) 93, Board of Governors of the Federal Reserve System (U.S.).
- 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.
- 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.
- Proietti, Tommaso, 2007. "Signal extraction and filtering by linear semiparametric methods," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 52(2), pages 935-958, October.
- 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.
- Tommaso Proietti, 2004. "Forecasting and Signal Extraction with Misspecified Models," Econometrics, EconWPA 0401002, EconWPA.
- 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.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (ZBW - German National Library of Economics).
If references are entirely missing, you can add them using this form.