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Forecasting with Dynamic Models using Shrinkage-based Estimation

Author

Listed:
  • Andrea Carriero

    () (Queen Mary, University of London)

  • George Kapetanios

    () (Queen Mary, University of London)

  • Massimiliano Marcellino

    () (Bocconi University and EUI)

Abstract

The paper provides a proof of consistency of the ridge estimator for regressions where the number of regressors tends to infinity. Such result is obtained without assuming a factor structure. A Monte Carlo study suggests that shrinkage autoregressive models can lead to very substantial advantages compared to standard autoregressive models. An empirical application focusing on forecasting inflation and GDP growth in a panel of countries confirms this finding.

Suggested Citation

  • Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2008. "Forecasting with Dynamic Models using Shrinkage-based Estimation," Working Papers 635, Queen Mary University of London, School of Economics and Finance.
  • Handle: RePEc:qmw:qmwecw:wp635
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    File URL: http://www.econ.qmul.ac.uk/media/econ/research/workingpapers/archive/wp635.pdf
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    Cited by:

    1. Gustavo Fruet Dias & George Kapetanios, 2014. "Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets," CREATES Research Papers 2014-37, Department of Economics and Business Economics, Aarhus University.
    2. repec:wsi:ijfexx:v:04:y:2017:i:01:n:s2424786317500074 is not listed on IDEAS

    More about this item

    Keywords

    Shrinkage; Forecasting;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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