Forecasting Based on Common Trends in Mixed Frequency Samples

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.

[1]  Maximo Camacho,et al.  Introducing the Euro-Sting: Short-Term Indicator of Euro Area Growth , 2009 .

[2]  Massimiliano Marcellino,et al.  Some Consequences of Temporal Aggregation in Empirical Analysis , 1999 .

[3]  J. Stock,et al.  A Probability Model of the Coincident Economic Indicators , 1988 .

[4]  Haiyan Song,et al.  Recent Developments in Econometric Modeling and Forecasting , 2005 .

[5]  Andrew Harvey,et al.  Forecasting, Structural Time Series Models and the Kalman Filter. , 1991 .

[6]  M. Friedman.  The Interpolation of Time Series by Related Series , 1962 .

[7]  Francis X. Diebold,et al.  Cointegration and Long-Horizon Forecasting , 1997, SSRN Electronic Journal.

[8]  S. Johansen.  STATISTICAL ANALYSIS OF COINTEGRATION VECTORS , 1988 .

[9]  Clive W. J. Granger,et al.  Linking series generated at different frequencies , 2008 .

[10]  Tommaso Proietti,et al.  Dynamic factor analysis with non‐linear temporal aggregation constraints , 2006 .

[11]  C. Witt,et al.  Forecasting tourism demand: A review of empirical research , 1995 .

[12]  Byeongchan Seong,et al.  Estimation of vector error correction models with mixed‐frequency data , 2013 .

[13]  J. Bai,et al.  Principal components estimation and identification of static factors , 2013 .

[14]  Jurgen A. Doornik,et al.  Statistical algorithms for models in state space using SsfPack 2.2 , 1999 .

[15]  Haiyan Song,et al.  Tourism demand modelling and forecasting—A review of recent research , 2008 .

[16]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

[17]  W. Fuller,et al.  Distribution of the Estimators for Autoregressive Time Series with a Unit Root , 1979 .

[18]  Siem Jan Koopman,et al.  Tracking the Business Cycle of the Euro Area , 2006 .

[19]  Michael P. Clements,et al.  Forecasting US output growth using leading indicators: an appraisal using MIDAS models , 2009 .

[20]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[21]  Serena Ng,et al.  Principal Components Estimation and Identication of the Factors , 2011 .

[22]  Francis X. Diebold,et al.  Real-Time Measurement of Business Conditions , 2007 .

[23]  Luís Catela Nunes,et al.  Nowcasting quarterly GDP growth in a monthly coincident indicator model , 2005 .

[24]  Namwon Hyung.  Linking series generated at different frequencies and its applications , 1998 .

[25]  David H. Small,et al.  Nowcasting: the real time informational content of macroeconomic data releases , 2008 .

[26]  R. Mariano,et al.  A New Coincident Index of Business Cycles Based on Monthly and Quarterly Series , 2002 .

[27]  Alain Hecq,et al.  Forecasting Mixed Frequency Time Series with ECM-MIDAS Models , 2012 .

[28]  Michael P. Clements,et al.  Macroeconomic Forecasting With Mixed-Frequency Data , 2008 .

[29]  Domenico Giannone,et al.  Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases , 2005 .

[30]  Siem Jan Koopman,et al.  Business and Default Cycles for Credit Risk , 2003 .

[31]  A. Harvey,et al.  Forecasting and Interpolation Using Vector Autoregressions with Common Trends , 1987 .