Forecasting Based on Common Trends in Mixed Frequency Samples

Peter Fuleky and Carl Bonham

No 201110, Working Papers from University of Hawaii at Manoa, Department of Economics

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.

Keywords: Dynamic Factor Model; Mixed Frequency Samples; Common Trends; Forecasting; Tourism Industry (search for similar items in EconPapers)
JEL-codes: C32 C53 E37 L83 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2011-06-13
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-tur
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http://www.economics.hawaii.edu/research/workingpapers/WP_11-10.pdf First version, 2011 (application/pdf)

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Working Paper: Forecasting Based on Common Trends in Mixed Frequency Samples (2013)
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