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Forecasting Based on Common Trends in Mixed Frequency Samples

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  • Peter Fuleky

    ()
    (UHERO and Department of Economics, University of Hawaii)

  • Carl S. Bonham

    ()
    (UHERO and Department of Economics, University of Hawaii)

Abstract

We extend the existing literature on small mixed frequency single factor models by allowing for multiple factors, considering indicators in levels, and allowing for cointegration among the indicators. We capture the cointegrating relationships among the indicators by common factors modeled as stochastic trends. We show that the stationary single-factor model frequently used in the literature is misspecified if the data set contains common stochastic trends. We find that taking advantage of common stochastic trends improves forecasting performance over a stationary single-factor model. The common-trends factor model outperforms the stationary single-factor model at all analyzed forecast horizons on a root mean squared error basis. Our results suggest that when the constituent indicators are integrated and cointegrated, modeling common stochastic trends, as opposed to eliminating them, will improve forecasts.

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File URL: http://www.economics.hawaii.edu/research/workingpapers/WP_11-10.pdf
File Function: First version, 2011
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Bibliographic Info

Paper provided by University of Hawaii at Manoa, Department of Economics in its series Working Papers with number 201110.

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Length: 31 pages
Date of creation: 13 Jun 2011
Date of revision:
Handle: RePEc:hai:wpaper:201110

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Keywords: Dynamic Factor Model; Mixed Frequency Samples; Common Trends; Forecasting; Tourism Industry;

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