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Boosting techniques for nonlinear time series models

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  • Nikolay Robinzonov

    ()

  • Gerhard Tutz

    ()

  • Torsten Hothorn

    ()

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    Abstract

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    File URL: http://hdl.handle.net/10.1007/s10182-011-0163-4
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    Bibliographic Info

    Article provided by Springer in its journal AStA Advances in Statistical Analysis.

    Volume (Year): 96 (2012)
    Issue (Month): 1 (January)
    Pages: 99-122

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    Handle: RePEc:spr:alstar:v:96:y:2012:i:1:p:99-122

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    Web page: http://www.springerlink.com/link.asp?id=112915

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    Related research

    Keywords: Componentwise boosting; Forecasting; Nonlinear times series; Autoregressive additive models; Lag selection;

    References

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    1. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, Elsevier, vol. 22(4), pages 679-688.
    2. Graham Elliott & Allan Timmermann, 2008. "Economic Forecasting," Journal of Economic Literature, American Economic Association, vol. 46(1), pages 3-56, March.
    3. James H. Stock & Mark W. Watson, 2001. "Forecasting output and inflation: the role of asset prices," Proceedings, Federal Reserve Bank of San Francisco, Federal Reserve Bank of San Francisco, issue Mar.
    4. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, Elsevier, vol. 13(2), pages 281-291, June.
    5. Ulrich Fritsche & Sabine Stephan, 2000. "Leading Indicators of German Business Cycles: An Assessment of Properties," Discussion Papers of DIW Berlin 207, DIW Berlin, German Institute for Economic Research.
    6. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, Econometric Society, vol. 79(2), pages 453-497, 03.
    7. David Hendry & Guillaume Chevillon, 2004. "Non-Parametric Direct Multi-step Estimation for Forecasting Economic Processes," Economics Series Working Papers 196, University of Oxford, Department of Economics.
    8. Massimiliano Marcellino & James Stock & Mark Watson, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," Working Papers 285, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    9. Audrino, Francesco & Barone-Adesi, Giovanni, 2006. "A dynamic model of expected bond returns: A functional gradient descent approach," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 51(4), pages 2267-2277, December.
    10. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 13(3), pages 253-63, July.
    11. Michael P. Clements & Philip Hans Franses & Norman R. Swanson, 2003. "Forecasting economic and financial time-series with non-linear models," Departmental Working Papers, Rutgers University, Department of Economics 200309, Rutgers University, Department of Economics.
    12. Timo Teräsvirta & Dick van Dijk & Marcelo Cunha Medeiros, 2004. "Linear models, smooth transition autoregressions and neural networks for forecasting macroeconomic time series: A reexamination," Textos para discussão 485, Department of Economics PUC-Rio (Brazil).
    13. Bernhard Pfaff, . "VAR, SVAR and SVEC Models: Implementation Within R Package vars," Journal of Statistical Software, American Statistical Association, American Statistical Association, vol. 27(i04).
    14. Francesco Audrino & Peter Bühlmann, 2007. "Splines for Financial Volatility," University of St. Gallen Department of Economics working paper series 2007, Department of Economics, University of St. Gallen 2007-11, Department of Economics, University of St. Gallen.
    15. Christian Dreger & Christian Schumacher, 2005. "Out-of-sample Performance of Leading Indicators for the German Business Cycle: Single vs. Combined Forecasts," Journal of Business Cycle Measurement and Analysis, OECD Publishing,CIRET, vol. 2005(1), pages 71-87.
    16. Shafik, Nivien & Tutz, Gerhard, 2009. "Boosting nonlinear additive autoregressive time series," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 53(7), pages 2453-2464, May.
    17. Jianhua Z. Huang & Lijian Yang, 2004. "Identification of non-linear additive autoregressive models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(2), pages 463-477.
    18. Francesco Audrino, 2012. "What Drives Short Rate Dynamics? A Functional Gradient Descent Approach," Computational Economics, Society for Computational Economics, vol. 39(3), pages 315-335, March.
    19. Tschernig, Rolf & Yang, Lijian, 1997. "Nonparametric lag selection for time series," SFB 373 Discussion Papers 1997,59, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    20. Claveria, Oscar & Pons, Ernest & Ramos, Raul, 2007. "Business and consumer expectations and macroeconomic forecasts," International Journal of Forecasting, Elsevier, Elsevier, vol. 23(1), pages 47-69.
    21. repec:wop:humbsf:1997-59 is not listed on IDEAS
    22. Schmid, Matthias & Hothorn, Torsten, 2008. "Boosting additive models using component-wise P-Splines," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 53(2), pages 298-311, December.
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    Cited by:
    1. Souhaib Ben Taieb & Rob J Hyndman, 2014. "Boosting multi-step autoregressive forecasts," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics 13/14, Monash University, Department of Econometrics and Business Statistics.

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