Penalized Splines, Mixed Models and the Wiener-Kolmogorov Filter
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- Danthine, Jean-Pierre & Girardin, Michel, 1989. "Business cycles in Switzerland : A comparative study," European Economic Review, Elsevier, vol. 33(1), pages 31-50, January.
- Yuedong Wang, 1998. "Mixed effects smoothing spline analysis of variance," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(1), pages 159-174.
- Hodrick, Robert J & Prescott, Edward C, 1997.
"Postwar U.S. Business Cycles: An Empirical Investigation,"
Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(1), pages 1-16, February.
- Robert J. Hodrick & Edward Prescott, 1981. "Post-War U.S. Business Cycles: An Empirical Investigation," Discussion Papers 451, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
- Flaig Gebhard, 2015.
"Why We Should Use High Values for the Smoothing Parameter of the Hodrick-Prescott Filter,"
Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 235(6), pages 518-538, December.
- Gebhard Flaig, 2012. "Why We Should Use High Values for the Smoothing Parameter of the Hodrick-Prescott Filter," CESifo Working Paper Series 3816, CESifo.
- Göran Kauermann & Jean D. Opsomer, 2011. "Data-driven selection of the spline dimension in penalized spline regression," Biometrika, Biometrika Trust, vol. 98(1), pages 225-230.
- Schlicht, Ekkehart, 2004.
"Estimating the Smoothing Parameter in the So-Called Hodrick-Prescott Filter,"
Discussion Papers in Economics
304, University of Munich, Department of Economics.
- Schlicht, Ekkehart, 2004. "Estimating the Smoothing Parameter in the So-Called Hodrick-Prescott Filter," IZA Discussion Papers 1054, Institute of Labor Economics (IZA).
- Tommaso Proietti, 2005.
"Forecasting and signal extraction with misspecified models,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(8), pages 539-556.
- Tommaso Proietti, 2004. "Forecasting and Signal Extraction with Misspecified Models," Econometrics 0401002, University Library of Munich, Germany.
- Gebhard Flaig*, 2005.
"Time Series Properties of the German Production Index,"
AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 89(4), pages 419-434, November.
- Flaig, Gebhard, 2005. "Time series properties of the German production index," Munich Reprints in Economics 20377, University of Munich, Department of Economics.
- Kauermann Goeran & Krivobokova Tatyana & Semmler Willi, 2011. "Filtering Time Series with Penalized Splines," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(2), pages 1-28, March.
- Krivobokova, Tatyana & Kauermann, Goran, 2007. "A Note on Penalized Spline Smoothing With Correlated Errors," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1328-1337, December.
- Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, January.
- Gerda Claeskens & Tatyana Krivobokova & Jean D. Opsomer, 2009. "Asymptotic properties of penalized spline estimators," Biometrika, Biometrika Trust, vol. 96(3), pages 529-544.
- Proietti, Tommaso, 2007. "Signal extraction and filtering by linear semiparametric methods," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 935-958, October.
- McElroy, Tucker, 2008. "Matrix Formulas For Nonstationary Arima Signal Extraction," Econometric Theory, Cambridge University Press, vol. 24(4), pages 988-1009, August.
- Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, January.
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More about this item
Keywords
Hodrick-Prescott filter; mixed models; penalized splines; trend estimation; Wiener-Kolmogorov filter;All these keywords.
JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2014-09-29 (Econometrics)
- NEP-ETS-2014-09-29 (Econometric Time Series)
- NEP-FOR-2014-09-29 (Forecasting)
- NEP-GER-2014-09-29 (German Papers)
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