Assessing the Macroeconomic Forecasting Performance of Boosting - Evidence for the United States, the Euro Area, and Germany
The use of large datasets for macroeconomic forecasting has received a great deal of interest recently. Boosting is one possible method of using high-dimensional data for this purpose. It is a stage-wise additive modelling procedure, which, in a linear specification, becomes a variable selection device that iteratively adds the predictors with the largest contribution to the fit. Using data for the United States, the euro area and Germany, we assess the performance of boosting when forecasting a wide range of macroeconomic variables. Moreover, we analyse to what extent its forecasting accuracy depends on the method used for determining its key regularisation parameter, the number of iterations. We find that boosting mostly outperforms the autoregressive benchmark, and that K-fold cross-validation works much better as stopping criterion than the commonly used information criteria.
|Date of creation:||2013|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: +49 (89) 9224-0
Fax: +49 (89) 985369
Web page: http://www.cesifo.de
More information through EDIRC
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.:
- Fabio Trojani, 2007.
"Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent,"
Journal of Financial Econometrics,
Society for Financial Econometrics, vol. 5(4), pages 591-623, Fall.
- Francesco Audrino & Fabio Trojani, 2007. "Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent," University of St. Gallen Department of Economics working paper series 2007 2007-24, Department of Economics, University of St. Gallen.
- Katja Drechsel & Rolf Scheufele, 2012.
"Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment,"
2012-16, Swiss National Bank.
- Katja Drechsel & R. Scheufele, 2013. "Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment," IWH Discussion Papers 7, Halle Institute for Economic Research.
- Steffen Henzel & Malte Rengel, 2014.
"Dimensions of Macroeconomic Uncertainty: A Common Factor Analysis,"
CESifo Working Paper Series
4991, CESifo Group Munich.
- Steffen Henzel & Malte Rengel, 2013. "Dimensions of macroeconomic uncertainty: A common factor analysis," Ifo Working Paper Series Ifo Working Paper No. 167, Ifo Institute for Economic Research at the University of Munich.
- Domenico Giannone & Lucrezia Reichlin & Luca Sala, 2005.
"Monetary Policy in Real Time,"
284, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
- Giannone, Domenico & Reichlin, Lucrezia & Sala, Luca, 2005. "Monetary Policy in Real Time," CEPR Discussion Papers 4981, C.E.P.R. Discussion Papers.
- Lucrezia Reichlin & Domenico Giannone & Luca Sala, . "Monetary policy in real time," ULB Institutional Repository 2013/10177, ULB -- Universite Libre de Bruxelles.
- Domenico Giannone & Lucrezia Reichlin & Luca Sala, 2005. "Monetary policy in real time," ULB Institutional Repository 2013/6401, ULB -- Universite Libre de Bruxelles.
- Julián Andrada-Félix & Fernando Fernández-Rodr�guez, 2008. "Improving moving average trading rules with boosting and statistical learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(5), pages 433-449.
- Jana Eklund & George Kapetanios, 2008.
"A Review of Forecasting Techniques for Large Data Sets,"
National Institute Economic Review,
National Institute of Economic and Social Research, vol. 203(1), pages 109-115, January.
- Jana Eklund & George Kapetanios, 2008. "A Review of Forecasting Techniques for Large Data Sets," Working Papers 625, Queen Mary University of London, School of Economics and Finance.
- Audrino, Francesco & Barone-Adesi, Giovanni, 2005. "Functional gradient descent for financial time series with an application to the measurement of market risk," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 959-977, April.
- Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2009.
"Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models,"
Economics Working Papers
ECO2009/31, European University Institute.
- Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2011. "Forecasting large datasets with Bayesian reduced rank multivariate models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 735-761, 08.
- Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2009. "Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models," CEPR Discussion Papers 7446, C.E.P.R. Discussion Papers.
- Nikolay Robinzonov & Gerhard Tutz & Torsten Hothorn, 2012. "Boosting techniques for nonlinear time series models," AStA Advances in Statistical Analysis, Springer, vol. 96(1), pages 99-122, January.
- Huyn Hak Kim & Norman R. Swanson, 2011.
"Forecasting Financial and Macroeconomic Variables Using Data Reduction Methods: New Empirical Evidence,"
Departmental Working Papers
201119, Rutgers University, Department of Economics.
- Kim, Hyun Hak & Swanson, Norman R., 2014. "Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence," Journal of Econometrics, Elsevier, vol. 178(P2), pages 352-367.
- Jushan Bai & Serena Ng, 2009. "Boosting diffusion indices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 607-629.
- Buchen, Teresa & Wohlrabe, Klaus, 2011.
"Forecasting with many predictors: Is boosting a viable alternative?,"
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.
- Shafik, Nivien & Tutz, Gerhard, 2009. "Boosting nonlinear additive autoregressive time series," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2453-2464, May.
When requesting a correction, please mention this item's handle: RePEc:ces:ceswps:_4148. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Julio Saavedra)
If references are entirely missing, you can add them using this form.