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Comparison of Bayesian moving Average and Principal Component Forecast for Large Dimensional Factor Models

  • Rachida Ouysse


    (School of Economics, The University of New South Wales)

The growing availability of financial and macroeconomic data sets including a large number of time series (hence the high dimensionality) calls for econometric methods providing a convenient and parsimonious representation of the covariance structure both in the time and the cross-sectional dimensions. Currently, dynamic factor models constitute the dominant framework across many disciplines for formal compression of information. To overcome the challenges of dimensionality, many forecast approaches proceed by somehow reducing the number of predictors. Principal component regression (PCR) approach proposes computing forecasts as projection on the first few principal components of the predictors. Bayesian model averaging (BMA) approach combines forecasts to extract information from different possible relationships between the predicted variable and the predictor variables. These two literature apparently moved in two different directions. However, recent findings by De Mol et al. [2008] and the Ouysse and Kohn [2009] suggest there are theoretical and practical reasons to connect the two literatures. This paper provides empirical evidence for connecting these two seemingly different approaches to forecasting. The empirical results serve as a preliminary guide to understanding the behaviour of BMA under double asymptotics, i.e. when the cross-section and the sample size become large.

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Paper provided by School of Economics, The University of New South Wales in its series Discussion Papers with number 2012-03.

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Length: 8 pages
Date of creation: Apr 2011
Date of revision:
Handle: RePEc:swe:wpaper:2012-03
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  1. Carmen Fernandez & Eduardo Ley & Mark F J Steel, 1998. "Benchmark priors for Bayesian model averaging," ESE Discussion Papers 26, Edinburgh School of Economics, University of Edinburgh.
  2. Ouysse, Rachida, 2006. "Consistent variable selection in large panels when factors are observable," Journal of Multivariate Analysis, Elsevier, vol. 97(4), pages 946-984, April.
  3. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2006. "Forecasting using a large number of predictors: is Bayesian regression a valid alternative to principal components?," Discussion Paper Series 1: Economic Studies 2006,32, Deutsche Bundesbank, Research Centre.
  4. Ouysse, Rachida & Kohn, Robert, 2010. "Bayesian variable selection and model averaging in the arbitrage pricing theory model," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3249-3268, December.
  5. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
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