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The cyclical component factor model

Author

Listed:
  • Christian M. Dahl
  • Henrik Hansen
  • John Smidt

    () (School of Economics and Management, University of Aarhus, Denmark and CREATES)

Abstract

Forecasting using factor models based on large data sets have received ample attention due to the models’ ability to increase forecast accuracy with respect to a range of key macroeconomic variables in the US and the UK. However, forecasts based on such factor models do not uniformly outperform the simple autoregressive model when using data from other countries. In this paper we propose to estimate the factors based on the pure cyclical components of the series entering the large data set. Monte Carlo evidence and an empirical illustration using Danish data shows that this procedure can indeed improve on pseudo real time forecast accuracy.

Suggested Citation

  • Christian M. Dahl & Henrik Hansen & John Smidt, 2008. "The cyclical component factor model," CREATES Research Papers 2008-44, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2008-44
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    References listed on IDEAS

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    Cited by:

    1. Arvid Raknerud & Terje Skjerpen & Anders Rygh Swensen, 2010. "Forecasting key macroeconomic variables from a large number of predictors: a state space approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(4), pages 367-387.
    2. Moral Carcedo, Julian & Perez García, Julian, 2015. "Feeding Large Econometric Models by a Mixed Approach of Classical Decomposition of Series and Dynamic Factor Analysis: Application to Wharton-UAM Model/Alimentando grandes modelos econométricos median," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 33, pages 487-512, Mayo.

    More about this item

    Keywords

    Factor model; Cyclical components; Estimation; Real time 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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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