Assessing nowcast accuracy of US GDP growth in real time: the role of booms and busts

  • Boriss SiliverstovsEmail author


In this paper, we reassess the forecasting performance of the Bayesian mixed-frequency model suggested in Carriero et al. (2015) in terms of point and density forecasts of the GDP growth rate using US macroeconomic data. Following Chauvet and Potter (2013), we evaluate the forecasting accuracy of the model relative to a univariate AR(2) model separately for expansions and recessions, as defined by the NBER business cycle chronology, rather than relying on a comparison of forecast accuracy over the whole forecast sample spanning 1985Q1–2011Q3. We find that most of the evidence favouring the more sophisticated model over the simple benchmark model is due to relatively few observations during recessions, especially those during the Great Recession. In contrast, during expansions, the gains in forecasting accuracy over the benchmark model are at best very modest. This implies that the relative forecasting performance of the models varies with business cycle phases. Ignoring this fact results in a distorted picture, the relative performance of the more sophisticated model in comparison with the naive benchmark model tends to be overstated during expansions and understated during recessions.


Nowcasting Mixed-frequency data Real-time data Business cycle 

JEL Classification

C22 C53 


Supplementary material

181_2019_1704_MOESM1_ESM.pdf (337 kb)
Supplementary material 1 (pdf 337 KB)


  1. Carriero, A, Clark TE, Marcellino M (2013) Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility. Working Paper 9312, Centre for Economic policy Research, LondonGoogle Scholar
  2. Carriero A, Clark TE, Marcellino M (2015) Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility. J Royal Stat Soc Ser A 178(4):837–862Google Scholar
  3. Chan JCC (2017) The stochastic volatility in mean model with time-varying parameters: an application to inflation modeling. J Bus Econ Stat 35(1):17–28Google Scholar
  4. Chauvet M, Potter S (2013) Forecasting output. In: Elliott G, Timmermann A (eds) Handbook of forecasting, vol 2. North Holland, Amsterdam, pp 1–56Google Scholar
  5. Clark TE (2011) Real-time density forecasts from Bayesian vector autoregressions with stochastic volatility. J Bus Econ Stat 29(3):327–341Google Scholar
  6. Croushore D, Stark T (2001) A real-time data set for macroeconomists. J Econ 105(1):111–130Google Scholar
  7. Croushore D, Stark T (2003) A real-time data set for macroeconomists: does the data vintage matter? Rev Econ Stat 85(3):605–617Google Scholar
  8. Edge RM, Kiley MT, Laforte J-P (2010) A comparison of forecast performance between federal reserve staff forecasts, simple reduced-form models, and a DSGE model. J Appl Econ 25(4):720–754Google Scholar
  9. Foroni C, Marcellino M (2013) A survey of econometric methods for mixed-frequency data. Working Paper 2013/06, Norges BankGoogle Scholar
  10. Foroni C, Marcellino M, Schumacher C (2015) Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials. J Royal Stat Soc Ser A 178(1):57–82Google Scholar
  11. Geweke J, Amisano G (2010) Comparing and evaluating bayesian predictive distributions of asset returns. Int J Forecast 26(2):216–230 Special Issue: Bayesian Forecasting in EconomicsGoogle Scholar
  12. Ghysels E, Santa-Clara P, Valkanov R (2004) The MIDAS touch: Mixed data sampling regression models. CIRANO Working Papers 2004s-20, CIRANOGoogle Scholar
  13. Ghysels E, Sinko A, Valkanov R (2007) MIDAS regressions: further results and new directions. Econ Rev 26(1):53–90Google Scholar
  14. Giannone D, Monti F, Reichlin L (2016) Exploiting the monthly data flow in structural forecasting. J Monet Econ 84:201–215Google Scholar
  15. Gneiting T, Katzfuss M (2014) Probabilistic forecasting. Annu Rev Stat Appl 1(1):125–151Google Scholar
  16. Guérin P, Marcellino M (2013) Markov-switching MIDAS models. J Bus Econ Stat 31(1):45–56Google Scholar
  17. Kim HH, Swanson NR (2018) Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods. Int J Forecast 34(2):339–354Google Scholar
  18. Marcellino M, Porqueddu M, Venditti F (2016) Short-term GDP forecasting with a mixed-frequency dynamic factor model with stochastic volatility. J Bus Econ Stat 34(1):118–127Google Scholar
  19. Marcellino M, Schumacher C (2010) Factor MIDAS for nowcasting and forecasting with ragged-edge data: a model comparison for German GDP. Oxford Bull Econ Stat 72(4):518–550Google Scholar
  20. Mazzi GL, Mitchell J, Montana G (2014) Density nowcasts and model combination: nowcasting Euro-Area GDP growth over the 2008–09 recession. Oxford Bull Econ Stat 76(2):233–256Google Scholar
  21. McCracken MW, Owyang MT, Sekhposyan T (2015) Real-Time Forecasting with a Large, Mixed Frequency, Bayesian VAR. Working Papers 2015-30, Federal Reserve Bank of St. LouisGoogle Scholar
  22. Mikosch H, Neuwirth S (2015) Real-Time Forecasting with a MIDAS VAR. KOF Working papers 15-377, KOF Swiss Economic Institute, ETH ZurichGoogle Scholar
  23. R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  24. Schorfheide F, Song D (2015) Real-time forecasting with a mixed-frequency VAR. J Bus Econ Stat 33(3):366–380Google Scholar
  25. Siliverstovs B (2017) Short-term forecasting with mixed-frequency data: a MIDASSO approach. Appl Econ 49(13):1326–1343Google Scholar
  26. Welch I, Goyal A (2008) A comprehensive look at the empirical performance of equity premium prediction. Rev Financial Stud 21(4):1455–1508Google Scholar
  27. Zhang B, Chan J, Cross J (2018) Stochastic Volatility Models with ARMA Innovations: An Application to G7 Inflation Forecasts. CAMA working paper no. 32/2018Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Bank of LatviaRigaLatvia
  2. 2.KOF Swiss Economic Institute, ETH ZürichZurichSwitzerland

Personalised recommendations