Skip to main content
SpringerLink
Log in
Book cover

Empirical Economic and Financial Research pp 61–80Cite as

Regularization Methods in Economic Forecasting

  • Chapter
  • First Online:

Part of the book series: Advanced Studies in Theoretical and Applied Econometrics ((ASTA,volume 48))

Abstract

Modern regularization techniques are able to select the relevant variables and features in prediction problems where much more predictors than observations are available. We investigate how regularization methods can be used to select the influential predictors of an autoregressive model with a very large number of potentially informative predictors. The methods are used to forecast the quarterly gross added value in the manufacturing sector by use of the business survey data collected by the ifo Institute. Also ensemble methods, which combine several forecasting methods are exemplarily evaluated.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Armstrong, J. (2001). Combining forecasts. International Series in Operations Research and Management Science, 30, 417–440.

    Article  Google Scholar 

  • Armstrong, J. S., & Collopy, F. (1992). Error measures for generalizing about forecasting methods: Empirical comparisons. International Journal of Forecasting, 8, 69–80.

    Article  Google Scholar 

  • Bai, J., & Ng, S. (2002). Determining the number of factors in approximate factor models. Econometrica, 70(1), 191–221.

    Article  Google Scholar 

  • Bai, J., & Ng, S. (2008). Forecasting economic time series using targeted predictors. Journal of Econometrics, 146(2), 304–317.

    Article  Google Scholar 

  • Bai, J., & Ng, S. (2009). Boosting diffusion indices. Journal of Applied Econometrics, 24(4), 607–629.

    Article  Google Scholar 

  • Becker, S. O., & Wohlrabe, K. (2007). Micro Data at the Ifo Institute for Economic Research - The “Ifo Business Survey”, Usage and Access. Ifo Working Paper Series Ifo Working Paper No. 47, Ifo Institute for Economic Research at the University of Munich.

    Google Scholar 

  • Buchen, T., & Wohlrabe, K. (2011). Forecasting with many predictors: Is boosting a viable alternative? Economics Letters, 113(1), 16–18.

    Article  Google Scholar 

  • Bühlmann, P., & Hothorn, T. (2007). Boosting algorithms: Regularization, prediction and model fitting (with discussion). Statistical Science, 22, 477–505.

    Article  Google Scholar 

  • Bühlmann, P., & Yu, B. (2003). Boosting with the L2 loss: Regression and classification. Journal of the American Statistical Association, 98, 324–339.

    Article  Google Scholar 

  • Clemen, R. T. (1989). Combining forecasts: A review and annotated bibliography. International Journal of Forecasting, 5(4), 559–583.

    Article  Google Scholar 

  • Clements, M. P., & Harvey, D. I. (2009). Forecast combination and encompassing. In Palgrave handbook of econometrics. Volume 2: Applied econometrics (pp. 169–198). London: Palgrave Macmillan.

    Google Scholar 

  • De Mol, C., Giannone, D., & Reichlin, L. (2008). Forecasting using a large number of predictors: Is bayesian shrinkage a valid alternative to principal components? Journal of Econometrics, 146(2), 318–328.

    Article  Google Scholar 

  • Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13, 253–263.

    Google Scholar 

  • Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004). Least angle regression. Annals of Statistics, 32, 407–499.

    Article  Google Scholar 

  • Feng, Y., & Heiler, S. (1998). Locally weighted autoregression. In Econometrics in theory and practice (pp. 101–117). Heidelberg: Springer.

    Chapter  Google Scholar 

  • Freund, Y., & Schapire, R. E. (1996). Experiments with a new boosting algorithm. In Proceedings of the Thirteenth International Conference on Medicine Learning (pp. 148–156). San Francisco: Morgan Kaufmann.

    Google Scholar 

  • Friedman, J. H. (2012). Fast sparse regression and classification. International Journal of Forecasting, 28(3), 722–738.

    Article  Google Scholar 

  • Friedman, J. H., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1–22.

    Google Scholar 

  • Goeman, J. J. (2010). L1 penalized estimation in the Cox proportional hazards model. Biometrical Journal, 52, 70–84.

    Google Scholar 

  • Harvey, D. I., Leybourne, S. J., & Newbold, P. (1998). Tests for forecast encompassing. Journal of Business & Economic Statistics, 16, 254–259.

    Google Scholar 

  • Hastie, T., Tibshirani, R., & Friedman, J. H. (2001). The elements of statistical learning. New York: Springer.

    Book  Google Scholar 

  • Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning (2nd ed.). New York: Springer.

    Book  Google Scholar 

  • Heiler, S., & Feng, Y. (2000). Data-driven decomposition of seasonal time series. Journal of Statistical Planning and Inference, 91(2), 351–363.

    Article  Google Scholar 

  • Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Bias estimation for nonorthogonal problems. Technometrics, 12, 55–67.

    Article  Google Scholar 

  • Kendall, M. G. (1957). A course in multivariate analysis. New York: Hafner Pub. Co.

    Google Scholar 

  • Kisinbay, T. (2010). The use of encompassing tests for forecast combinations. Journal of Forecasting, 29, 715–727.

    Article  Google Scholar 

  • Meier, L. (2009). grplasso: Fitting user specified models with Group Lasso penalty. R package version 0.4-2.

    Google Scholar 

  • Meier, L., van de Geer, S., & Bühlmann, P. (2008). The group lasso for logistic regression. Journal of the Royal Statistical Society, Series B, 70, 53–71.

    Article  Google Scholar 

  • Mevik, B.-H., Wehrens, R., & Liland, K. H. (2011). pls: Partial Least Squares and Principal Component regression. R package version 2.3-0.

    Google Scholar 

  • Robinzonov, N., Tutz, G., & Hothorn, T. (2012). Boosting techniques for nonlinear time series models. AStA Advances in Statistical Analysis, 96, 99–122.

    Article  Google Scholar 

  • Shafik, N., & Tutz, G. (2009). Boosting nonlinear additive autoregressive time series. Computational Statistics and Data Analysis, 53, 2453–2464.

    Article  Google Scholar 

  • Stock, J. H., & Watson, M. W. (2004). Combination forecasts of output growth in a seven-country data set. Journal of Forecasting, 23, 405–430.

    Article  Google Scholar 

  • Stock, J. H., & Watson, M. W. (2006). Forecasting with many predictors. In C. G. G. Elliott & A. Timmermann (Eds.), Handbook of economic forecasting (Vol. 1, pp. 515–554). Amsterdam: Elsevier.

    Chapter  Google Scholar 

  • Stock, J. H., & Watson, M. W. (2011, February). Generalized shrinkage methods for forecasting using many predictors generalized shrinkage methods for forecasting using many predictors. Manuscript, Harvard University, 0-62.

    Google Scholar 

  • Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society B, 58, 267–288.

    Google Scholar 

  • Wold, H. (1975). Soft Modeling by latent variables; The nonlinear iterative partial least squares approach. Perspectives in probability and statistics. Papers in Honour of M. S. Bartlett. London: Academic Press.

    Google Scholar 

  • Yuan, M., & Lin, Y. (2006). Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society B, 68, 49–67.

    Article  Google Scholar 

  • Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society B, 67, 301–320.

    Article  Google Scholar 

Download references

Acknowledgements

We thank the Munich ifo Institute for providing the data from the surveys of the ifo Business Climate Index. Especially, we thank Kai Carstensen, Johannes Mayr, Klaus Wohlrabe, Teresa Buchen and Steffen Henzel from the ifo Institute for helpful discussions.

Author information

Authors and Affiliations

  1. Department of Statistics, LMU Munich, München, Germany

    Gunther Schauberger & Gerhard Tutz

Authors
  1. Gunther Schauberger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gerhard Tutz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gerhard Tutz .

Editor information

Editors and Affiliations

  1. Dept. of Mathematics and Statistics, University of Konstanz, Konstanz, Germany

    Jan Beran

  2. Faculty of Business Administration and Economics, University of Paderborn, Paderborn, Germany

    Yuanhua Feng

  3. Helmut Schmidt University, Hamburg, Germany

    Hartmut Hebbel

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Schauberger, G., Tutz, G. (2015). Regularization Methods in Economic Forecasting. In: Beran, J., Feng, Y., Hebbel, H. (eds) Empirical Economic and Financial Research. Advanced Studies in Theoretical and Applied Econometrics, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-319-03122-4_4

Download citation

Publish with us

Policies and ethics

Search

Navigation