Forecasting with Mixed Frequencies

A dilemma faced by forecasters is that data are not all sampled at the same frequency. Most macroeconomic data are sampled monthly (e.g., employment) or quarterly (e.g., GDP). Most financial variables (e.g., interest rates and asset prices), on the other hand, are sampled daily or even more frequently. The challenge is how to best use available data. To that end, the authors survey some common methods for dealing with mixed-frequency data. They show that, in some cases, simply averaging the higher-frequency data produces no discernible disadvantage. In other cases, however, explicitly modeling the flow of data (e.g., using mixed data sampling as in Ghysels, Santa-Clara, and Valkanov, 2004) may be more beneficial to the forecaster, especially if the forecaster is interested in constructing intra-period forecasts.

[1]  Eric Ghysels,et al.  Regression Models with Mixed Sampling Frequencies , 2010 .

[2]  Eric Ghysels,et al.  Forecasting Professional Forecasters , 2006 .

[3]  E. Ghysels,et al.  There is a Risk-Return Tradeoff after All , 2004 .

[4]  M. Marcellino,et al.  Midas Versus Mixed-Frequency VAR: Nowcasting GDP in the Euro Area , 2009, SSRN Electronic Journal.

[5]  A. Sinko,et al.  Matlab Toolbox for Mixed Sampling Frequency Data Analysis using MIDAS Regression Models ∗ , 2009 .

[6]  C. Sims,et al.  Bayesian methods for dynamic multivariate models , 1998 .

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

[8]  Dean Croushore,et al.  A real-time data set for macroeconomists , 2001 .

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

[10]  Frank T. Magiera,et al.  There Is a Risk–Return Trade-Off After All , 2005 .

[11]  Roque B Fernandez,et al.  A Methodological Note on the Estimation of Time Series , 1981 .

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

[13]  Anthony S. Tay.  Mixing Frequencies: Stock Returns as a Predictor of Real Output Growth , 2006 .

[14]  E. Ghysels,et al.  MIDAS Regressions: Further Results and New Directions , 2006 .

[15]  Fulvio Corsi,et al.  A Simple Approximate Long-Memory Model of Realized Volatility , 2008 .

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

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

[18]  Dean Croushore,et al.  Do Consumer Confidence Indexes Help Forecast Consumer Spending in Real Time? , 2005, SSRN Electronic Journal.

[19]  Massimiliano Marcellino,et al.  Midas Vs. Mixed-Frequency VAR: Nowcasting GDP in the Euro Area , 2009 .

[20]  C. Granger,et al.  Handbook of Economic Forecasting , 2006 .

[21]  Robert R Bitmead,et al.  Smoothing Approaches to Reconstruction of Missing Data in Array Processing , 2001 .

[22]  Eric Ghysels,et al.  Série Scientifique Scientific Series the Midas Touch: Mixed Data Sampling Regression Models the Midas Touch: Mixed Data Sampling Regression Models* , 2022 .

[23]  E. Ghysels,et al.  Série Scientifique Scientific Series Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies , 2022 .

[24]  Dean Croushore,et al.  Forecasting with Real-Time Macroeconomic Data , 2006 .