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FORECASTING WITH MIXED-FREQUENCY FACTOR MODELS IN THE PRESENCE OF COMMON TRENDS

Published online by Cambridge University Press:  14 November 2013

Peter Fuleky and
Carl S. Bonham
Peter Fuleky*
Affiliation:
University of Hawaii
Carl S. Bonham
Affiliation:
University of Hawaii
*
Address correspondence to: Peter Fuleky, UHERO and Department of Economics, University of Hawaii, 542 Saunders Hall, 2424 Maile Way, Honolulu, HI 96822, USA; e-mail: fuleky@hawaii.edu.
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Abstract

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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 variables are cointegrated, modeling common stochastic trends improves forecasts.

Keywords

Dynamic Factor ModelMixed-Frequency SamplesCommon TrendsForecasting

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