A Survey of Econometric Methods for Mixed-Frequency Data

The development of models for variables sampled at different frequencies has attracted substantial interest in the recent econometric literature. In this paper we provide an overview of the most common techniques, including bridge equations, MIxed DAta Sampling (MIDAS) models, mixed frequency VARs, and mixed frequency factor models. We also consider alternative techniques for handling the ragged edge of the data, due to asynchronous publication. Finally, we survey the main empirical applications based on alternative mixed frequency models.

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

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

[3]  Massimiliano Marcellino,et al.  Selecting Predictors by Using Bayesian Model Averaging in Bridge Models , 2012 .

[4]  Jeremy Piger,et al.  The Use and Abuse of 'Real-Time' Data in Economic Forecasting , 2000 .

[5]  Ana Beatriz Galvão,et al.  Changes in predictive ability with mixed frequency data , 2013 .

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

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

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

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

[10]  Knut Are Aastveit,et al.  Short-Term Forecasting of GDP and Inflation in Real-Time : Norges Bank’s System for Averaging Models , 2011 .

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

[12]  Marco Lippi,et al.  The Generalized Dynamic Factor Model , 2002 .

[13]  Massimiliano Marcellino,et al.  U-Midas: Midas Regressions with Unrestricted Lag Polynomials , 2012, SSRN Electronic Journal.

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

[15]  K. Wohlrabe,et al.  Forecasting with mixed-frequency time series models , 2009 .

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

[17]  Massimiliano Marcellino,et al.  Interpolation and Backdating with a Large Information Set , 2003, SSRN Electronic Journal.

[18]  E. Ghysels,et al.  Why Do Absolute Returns Predict Volatility So Well , 2006 .

[19]  Massimiliano Marcellino,et al.  TEMPORAL DISAGGREGATION, MISSING OBSERVATIONS, OUTLIERS, AND FORECASTING , 1999 .

[20]  Paul Viefers,et al.  Bayesian Inference for the Mixed-Frequency VAR Model , 2011 .

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

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

[23]  Steven G. Lanning,et al.  Missing Observations: A Simultaneous Approach versus Interpolation by Related Series , 1986 .

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

[25]  J. Stock,et al.  Forecasting Using Principal Components From a Large Number of Predictors , 2002 .

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

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

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

[29]  D. H. Mellor,et al.  Real time , 1981 .

[30]  Bharat Trehan,et al.  Using monthly data to predict quarterly output , 1996 .

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

[32]  Massimiliano Marcellino,et al.  Survey Data as Coincident or Leading Indicators , 2009 .

[33]  D. Giannone,et al.  Now-Casting and the Real-time Data Flow , 2012, SSRN Electronic Journal.

[34]  Massimiliano Marcellino,et al.  A Comparison of Mixed Frequency Approaches for Modelling Euro Area Macroeconomic Variables , 2012 .

[35]  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 .

[36]  Riccardo Cristadoro,et al.  Short-Term Forecasting of GDP Using Large Monthly Datasets – A Pseudo Real-Time Forecast Evaluation Exercise , 2008 .

[37]  Olivier Darné,et al.  Monthly GDP Forecasting Using Bridge Models: Application for the French Economy , 2012 .

[38]  Massimiliano Marcellino,et al.  Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility , 2013 .

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

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

[41]  Eric Ghysels,et al.  Multi-Period Forecasts of Volatility: Direct, Iterated, and Mixed-Data Approaches , 2009 .

[42]  Michele Modugno,et al.  Maximum Likelihood Estimation of Factor Models on Data Sets with Arbitrary Pattern of Missing Data , 2010, SSRN Electronic Journal.

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

[44]  Gonzalo Camba-Mendez,et al.  Short-Term Forecasts of Euro Area GDP Growth , 2008, SSRN Electronic Journal.

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

[46]  Eric Ghysels,et al.  State Space Models and MIDAS Regressions , 2013 .

[47]  Libero Monteforte,et al.  Real Time Forecasts of Inflation: The Role of Financial Variables , 2008 .

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

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

[50]  Marta Bańbura,et al.  A Look into the Factor Model Black Box: Publication Lags and the Role of Hard and Soft Data in Forecasting GDP , 2007, SSRN Electronic Journal.

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

[52]  Tom Stark Does Current-Quarter Information Improve Quarterly Forecasts for the U.S. Economy? , 2000 .

[53]  Stefan Mittnik,et al.  Forecasting Quarterly German GDP at Monthly Intervals Using Monthly Ifo Business Conditions Data , 2004, SSRN Electronic Journal.

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

[55]  Andrew T. Foerster,et al.  Bayesian Mixed Frequency VARs , 2015 .

[56]  Leif Anders Thorsrud,et al.  Nowcasting GDP in Real Time: A Density Combination Approach , 2014 .

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

[58]  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.

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