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Forecasting with Mixed Frequency Samples: The Case of Common Trends

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  • Peter Fuleky

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

  • Carl S. Bonham

    ()
    (Department of Economics, University of Hawaii at Manoa)

Abstract

We analyze the forecasting performance of small mixed frequency factor models when the observed variables share stochastic trends. The indicators are observed at various frequencies and are tied together by cointegration so that valuable high frequency information is passed to low frequency series through the common factors. Differencing the data breaks the cointegrating link among the series and some of the signal leaks out to the idiosyncratic components, which do not contribute to the transfer of information among indicators. We find that allowing for common trends improves forecasting performance over a stationary factor model based on differenced data. The "common-trends factor model" outperforms the stationary factor model at all analyzed forecast horizons. Our results demonstrate that when mixed frequency vari- ables are cointegrated, modeling common stochastic trends improves forecasts.

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File URL: http://www.economics.hawaii.edu/research/workingpapers/WP_13-16R.pdf
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Bibliographic Info

Paper provided by University of Hawaii at Manoa, Department of Economics in its series Working Papers with number 201316.

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Length: 44 pages
Date of creation: Aug 2013
Date of revision:
Handle: RePEc:hai:wpaper:201316

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

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