A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates

In this paper, we focus on the different methods which have been proposed in the literature to date for dealing with mixed-frequency and ragged-edge datasets: bridge equations, mixed-data sampling (MIDAS), and mixed-frequency VAR (MF-VAR) models. We discuss their performances for nowcasting the quarterly growth rate of the Euro area GDP and its components, using a very large set of monthly indicators. We investigate the behaviors of single indicator models, forecast combinations and factor models, in a pseudo real-time framework. MIDAS with an AR component performs quite well, and outperforms MF-VAR at most horizons. Bridge equations perform well overall. Forecast pooling is superior to most of the single indicator models overall. Pooling information using factor models gives even better results. The best results are obtained for the components for which more economically related monthly indicators are available. Nowcasts of GDP components can then be combined to obtain nowcasts for the total GDP growth.

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

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

[3]  Allan Timmermann,et al.  Persistence in forecasting performance and conditional combination strategies , 2006 .

[4]  A. Timmermann.  Chapter 4 Forecast Combinations , 2006 .

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

[6]  R. Golinelli,et al.  Bridge models to forecast the euro area GDP , 2004 .

[7]  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 .

[8]  Massimiliano Marcellino,et al.  Factor Midas for Nowcasting and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP , 2008 .

[9]  Marie Diron,et al.  Short-Term Forecasts of Euro Area Real GDP Growth: An Assessment of Real-Time Performance Based on Vintage Data , 2006, SSRN Electronic Journal.

[10]  Paul Newbold,et al.  Testing the equality of prediction mean squared errors , 1997 .

[11]  Maximo Camacho,et al.  Markov-Switching Dynamic Factor Models in Real Time , 2012, International Journal of Forecasting.

[12]  F. Diebold,et al.  Real-Time Macroeconomic Monitoring: Real Activity, Inflation, and Interactions , 2010 .

[13]  Tim Schwarzmüller.  Model Pooling and Changes in the Informational Content of Predictors: an Empirical Investigation for the Euro Area , 2015 .

[14]  Massimiliano Marcellino,et al.  EUROMIND: a monthly indicator of the euro area economic conditions , 2011 .

[15]  Jörg Breitung,et al.  Real-Time Forecasting of GDP Based on a Large Factor Model with Monthly and Quarterly Data , 2007, SSRN Electronic Journal.

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

[17]  Peter Zadrozny,et al.  Gaussian Likelihood of Continuous-Time ARMAX Models When Data Are Stocks and Flows at Different Frequencies , 1988, Econometric Theory.

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

[19]  Michal Franta,et al.  Forecasting Czech GDP Using Mixed-Frequency Data Models , 2016 .

[20]  J. Stock,et al.  A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series , 1998 .

[21]  Massimiliano Marcellino,et al.  Markov-Switching MIDAS Models , 2011 .

[22]  Michael P. Clements,et al.  Macroeconomic Forecasting with Mixed Frequency Data: Forecasting Us Output Growth and Inflation , 2006 .

[23]  Yasutomo Murasawa,et al.  A Coincident Index, Common Factors, and Monthly Real GDP , 2010 .

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

[25]  Kenneth F. Wallis,et al.  Forecasting with an econometric model: The ‘ragged edge’ problem† , 1986 .

[26]  J. Stock,et al.  Macroeconomic Forecasting Using Diffusion Indexes , 2002 .

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

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

[29]  Christian Schumacher,et al.  POOLING VERSUS MODEL SELECTION FOR NOWCASTING GDP WITH MANY PREDICTORS: EMPIRICAL EVIDENCE FOR SIX INDUSTRIALIZED COUNTRIES , 2013 .

[30]  Frank Schorfheide,et al.  Real-Time Forecasting With a Mixed-Frequency VAR , 2013 .