A Comparison of Mixed Frequency Approaches for Modelling Euro Area Macroeconomic Variables

Forecast models that take into account unbalanced datasets have recently attracted substantial attention. In this paper, we focus on different methods pro- posed so far in the literature to deal with mixed-frequency and ragged-edge datasets: bridge equations, mixed-data sampling (MIDAS), and mixed-frequency (MF) models. We discuss their performance on now- and forecasting the quarterly growth rate of Euro area GDP and its components, using a very large set of monthly indicators taken from Eurostat dataset of Principal European Economic Indicators (PEEI). We both investigate the behavior of single indicator models and combine first the forecasts within each class of models and then the information in the dataset by means of factor models, in a pseudo real-time framework. Anticipating some of the results, MIDAS without an AR component performs worse than the corresponding approach which incorporates it, and MF-VAR seems to outperform the MIDAS approach only at longer horizons. Bridge equations have overall a good performance. Pooling many indicators within each class of models is overall superior to most of the single indicator models. Pooling information with the use of factor models gives even better results, at least at short horizons. A battery of robustness checks high- lights the importance of monthly information during the crisis more than in stable periods. Extending the analysis to a real-time context highlights that revisions do not influence substantially the results.

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

[2]  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 .

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

[4]  Michael P. Clements,et al.  Real-time forecasting of inflation and output growth with autoregressive models in the presence of data revisions , 2013 .

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

[6]  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.

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

[8]  R. Mariano,et al.  Constructing a Coincident Index of Business Cycles without Assuming a One-factor Model , 2004 .

[9]  A. Timmermann.  Forecast Combinations , 2005 .

[10]  Catherine Doz,et al.  A Two-Step Estimator for Large Approximate Dynamic Factor Models Based on Kalman Filtering , 2007 .

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

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

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

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

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

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

[17]  Christian Schumacher,et al.  Pooling versus Model Selection for Nowcasting with Many Predictors: An Application to German GDP , 2009, SSRN Electronic Journal.

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

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

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

[21]  Filippo Altissimo,et al.  New Eurocoin: Tracking Economic Growth in Real Time , 2006, The Review of Economics and Statistics.

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

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