Boosting nonlinear additive autoregressive time series
Author
Abstract
Suggested Citation
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Young, Peter C. & Pedregal, Diego J., 1999. "Macro-economic relativity: government spending, private investment and unemployment in the USA 1948-1998," Structural Change and Economic Dynamics, Elsevier, vol. 10(3-4), pages 359-380, December.
- Buhlmann P. & Yu B., 2003. "Boosting With the L2 Loss: Regression and Classification," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 324-339, January.
- Jianhua Z. Huang & Haipeng Shen, 2004. "Functional Coefficient Regression Models for Non‐linear Time Series: A Polynomial Spline Approach," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(4), pages 515-534, December.
- Hofmann, Marc & Gatu, Cristian & Kontoghiorghes, Erricos John, 2007. "Efficient algorithms for computing the best subset regression models for large-scale problems," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 16-29, September.
- Rolf Tschernig & Lijian Yang, 2000.
"Nonparametric Lag Selection for Time Series,"
Journal of Time Series Analysis, Wiley Blackwell, vol. 21(4), pages 457-487, July.
- 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.
- Gatu, Cristian & Kontoghiorghes, Erricos J. & Gilli, Manfred & Winker, Peter, 2008. "An efficient branch-and-bound strategy for subset vector autoregressive model selection," Journal of Economic Dynamics and Control, Elsevier, vol. 32(6), pages 1949-1963, June.
- Gerhard Tutz & Harald Binder, 2006. "Generalized Additive Modeling with Implicit Variable Selection by Likelihood-Based Boosting," Biometrics, The International Biometric Society, vol. 62(4), pages 961-971, December.
- De Gooijer, Jan G. & Ray, Bonnie K., 2003. "Modeling vector nonlinear time series using POLYMARS," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 73-90, February.
- 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, May.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Klaus Wohlrabe & Teresa Buchen, 2014.
"Assessing the Macroeconomic Forecasting Performance of Boosting: Evidence for the United States, the Euro Area and Germany,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 231-242, July.
- Teresa Buchen & Klaus Wohlrabe, 2013. "Assessing the Macroeconomic Forecasting Performance of Boosting - Evidence for the United States, the Euro Area, and Germany," CESifo Working Paper Series 4148, CESifo.
- Teresa, Buchen & Wohlrabe, Klaus, 2014. "Assessing the Macroeconomic Forecasting Performance of Boosting: Evidence for the United States, the Euro Area, and Germany," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100626, Verein für Socialpolitik / German Economic Association.
- Christmann, Andreas & Hable, Robert, 2012. "Consistency of support vector machines using additive kernels for additive models," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 854-873.
- Goessling, Marc, 2017. "LogitBoost autoregressive networks," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 88-98.
- Buchen, Teresa & Wohlrabe, Klaus, 2011.
"Forecasting with many predictors: Is boosting a viable alternative?,"
Economics Letters, Elsevier, vol. 113(1), pages 16-18, October.
- Buchen, Teresa & Wohlrabe, Klaus, 2010. "Forecasting with many predictors - Is boosting a viable alternative?," Discussion Papers in Economics 11788, University of Munich, Department of Economics.
- Souhaib Ben Taieb & Rob J Hyndman, 2014. "Boosting multi-step autoregressive forecasts," Monash Econometrics and Business Statistics Working Papers 13/14, Monash University, Department of Econometrics and Business Statistics.
- Marra, Giampiero & Wood, Simon N., 2011. "Practical variable selection for generalized additive models," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2372-2387, July.
- Nikolay Robinzonov & Gerhard Tutz & Torsten Hothorn, 2012. "Boosting techniques for nonlinear time series models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(1), pages 99-122, January.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Miao Yang & Lan Xue & Lijian Yang, 2016. "Variable selection for additive model via cumulative ratios of empirical strengths total," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(3), pages 595-616, September.
- Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
- Marra, Giampiero & Wood, Simon N., 2011. "Practical variable selection for generalized additive models," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2372-2387, July.
- Stefanie Hieke & Axel Benner & Richard F Schlenk & Martin Schumacher & Lars Bullinger & Harald Binder, 2016. "Identifying Prognostic SNPs in Clinical Cohorts: Complementing Univariate Analyses by Resampling and Multivariable Modeling," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-18, May.
- Chen, Xiaohong & Christensen, Timothy M., 2015.
"Optimal uniform convergence rates and asymptotic normality for series estimators under weak dependence and weak conditions,"
Journal of Econometrics, Elsevier, vol. 188(2), pages 447-465.
- Xiaohong Chen & Timothy M. Christensen, 2014. "Optimal uniform convergence rates and asymptotic normality for series estimators under weak dependence and weak conditions," CeMMAP working papers CWP46/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Xiaohong Chen & Timothy M. Christensen, 2014. "Optimal Uniform Convergence Rates and Asymptotic Normality for Series Estimators under Weak Dependence and Weak Conditions," Cowles Foundation Discussion Papers 1976, Cowles Foundation for Research in Economics, Yale University.
- Faisal Zahid & Gerhard Tutz, 2013. "Multinomial logit models with implicit variable selection," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(4), pages 393-416, December.
- Nikolay Robinzonov & Gerhard Tutz & Torsten Hothorn, 2012. "Boosting techniques for nonlinear time series models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(1), pages 99-122, January.
- Peter C. B. Phillips & Zhentao Shi, 2021.
"Boosting: Why You Can Use The Hp Filter,"
International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 521-570, May.
- Peter C.B. Phillips & Zhentao Shi, 2019. "Boosting: Why you Can Use the HP Filter," Cowles Foundation Discussion Papers 2212, Cowles Foundation for Research in Economics, Yale University.
- Peter C. B. Phillips & Zhentao Shi, 2019. "Boosting: Why You Can Use the HP Filter," Papers 1905.00175, arXiv.org, revised Nov 2020.
- Leitenstorfer, Florian & Tutz, Gerhard, 2007. "Knot selection by boosting techniques," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4605-4621, May.
- Xiaohong Chen & Timothy M. Christensen, 2014. "Optimal uniform convergence rates and asymptotic normality for series estimators under weak dependence and weak conditions," CeMMAP working papers 46/14, Institute for Fiscal Studies.
- Sariyar Murat & Schumacher Martin & Binder Harald, 2014. "A boosting approach for adapting the sparsity of risk prediction signatures based on different molecular levels," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(3), pages 343-357, June.
- 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.
- Gatu, Cristian & Yanev, Petko I. & Kontoghiorghes, Erricos J., 2007. "A graph approach to generate all possible regression submodels," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 799-815, October.
- Fossati, Sebastian, 2012.
"Covariate unit root tests with good size and power,"
Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3070-3079.
- Fossati, Sebastian, 2011. "Covariate Unit Root Tests with Good Size and Power," Working Papers 2011-4, University of Alberta, Department of Economics.
- Gao, Jiti, 2007. "Nonlinear time series: semiparametric and nonparametric methods," MPRA Paper 39563, University Library of Munich, Germany, revised 01 Sep 2007.
- Yehua Li & Marc G. Genton, 2009. "Single‐Index Additive Vector Autoregressive Time Series Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 369-388, September.
- Tutz, Gerhard & Binder, Harald, 2007. "Boosting ridge regression," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6044-6059, August.
- Huang, Lei & Jiang, Hui & Wang, Huixia, 2019. "A novel partial-linear single-index model for time series data," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 110-122.
- Li, Rui & Wan, Alan T.K. & You, Jinhong, 2016. "Semiparametric GMM estimation and variable selection in dynamic panel data models with fixed effects," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 401-423.
- Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:53:y:2009:i:7:p:2453-2464. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.