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Boosting techniques for nonlinear time series models

Listed author(s):
  • Nikolay Robinzonov

    ()

  • Gerhard Tutz

    ()

  • Torsten Hothorn

    ()

Registered author(s):

    No abstract is available for this item.

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    File URL: http://hdl.handle.net/10.1007/s10182-011-0163-4
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    Article provided by Springer & German Statistical Society 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
    DOI: 10.1007/s10182-011-0163-4
    Contact details of provider: Web page: http://www.springer.com

    Web page: http://www.dstatg.de/de/startseite/

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    1. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
    2. Chevillon, Guillaume & Hendry, David F., 2005. "Non-parametric direct multi-step estimation for forecasting economic processes," International Journal of Forecasting, Elsevier, vol. 21(2), pages 201-218.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Graham Elliott & Allan Timmermann, 2008. "Economic Forecasting," Journal of Economic Literature, American Economic Association, vol. 46(1), pages 3-56, March.
    5. Schmid, Matthias & Hothorn, Torsten, 2008. "Boosting additive models using component-wise P-Splines," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 298-311, December.
    6. Clements, Michael P. & Franses, Philip Hans & Swanson, Norman R., 2004. "Forecasting economic and financial time-series with non-linear models," International Journal of Forecasting, Elsevier, vol. 20(2), pages 169-183.
    7. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, 03.
    8. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    9. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
    10. Claveria, Oscar & Pons, Ernest & Ramos, Raul, 2007. "Business and consumer expectations and macroeconomic forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 47-69.
    11. Terasvirta, Timo & van Dijk, Dick & Medeiros, Marcelo C., 2005. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," International Journal of Forecasting, Elsevier, vol. 21(4), pages 755-774.
    12. Shafik, Nivien & Tutz, Gerhard, 2009. "Boosting nonlinear additive autoregressive time series," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2453-2464, May.
    13. 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, Centre for International Research on Economic Tendency Surveys, vol. 2005(1), pages 71-87.
    14. Francesco Audrino & Peter Bühlmann, 2009. "Splines for financial volatility," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 655-670.
    15. 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.
    16. Fritsche Ulrich & Stephan Sabine, 2002. "Leading Indicators of German Business Cycles. An Assessment of Properties / Frühindikatoren der deutschen Konjunktur. Eine Beurteilung ihrer Eigenschaften," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 222(3), pages 289-315, June.
    17. Pfaff, Bernhard, 2008. "VAR, SVAR and SVEC Models: Implementation Within R Package vars," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i04).
    18. Francesco Audrino, 2012. "What Drives Short Rate Dynamics? A Functional Gradient Descent Approach," Computational Economics, Springer;Society for Computational Economics, vol. 39(3), pages 315-335, March.
    19. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    20. Audrino, Francesco & Barone-Adesi, Giovanni, 2006. "A dynamic model of expected bond returns: A functional gradient descent approach," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2267-2277, December.
    21. 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.
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