Forecasting with Mixed Frequency Samples: The Case of Common Trends

Peter Fuleky and Carl Bonham

No 201305, Working Papers from University of Hawaii at Manoa, Department of Economics

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 fre- quency information is passed to low frequency series through the common factors. Di erencing 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 trans- fer of information among indicators. We nd that allowing for common trends improves forecasting performance over a stationary factor model based on di erenced 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 Model; Mixed Frequency Samples; Common Trends; Forecasting (search for similar items in EconPapers)
JEL-codes: C32 C53 E37 L83 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2013-04
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-mac and nep-mst
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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http://www.economics.hawaii.edu/research/workingpapers/WP_13-5.pdf First version, 2013 (application/pdf)

Related works:
Working Paper: Forecasting with Mixed Frequency Samples: The Case of Common Trends (2013)
Working Paper: Forecasting with Mixed Frequency Samples: The Case of Common Trends (2013)
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