Nowcasting (economics)

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The term ‘nowcasting’ is a contraction of ‘now’ and ‘forecasting’. The term is used in both economics and meteorology. This article is about its use in economics; see meteorology for its use referring to techniques used to make short-term weather forecasts, typically in the 0 to 12 hour range.[1]

Nowcasting has recently become popular in economics. Standard measures used to assess the state of an economy, e.g., gross domestic product (GDP), are only determined after a long delay, and are even then subject to subsequent revisions. While weather forecasters know weather conditions today and only have to predict the weather tomorrow, economists have to forecast the present and even the recent past.

Historically, nowcasting techniques have been based on simplified heuristic approaches. A recent paper by Giannone, Reichlin and Small (2008)[2] has shown that the process of nowcasting can be formalized in a statistical model which produces predictions without the need for informal judgement. The model exploits information from a large quantity of data series at different frequencies and with different publication lags. The idea is that signals about the direction of change in GDP can be extracted from this large and heterogeneous set of information sources (e.g., jobless figures, industrial orders, the trade balance, etc.) before GDP itself is published. In nowcasting this data is used to compute sequences of current quarter GDP estimates in relation to the real time flow of data releases.

Nowcasting models have been applied in many institutions, in particular Central Banks, and the technique is used routinely to monitor the state of the economy in real time. Selected academic research papers show how this technique has developed.[3][4][5][6][7][8] [9][10] Banbura, Giannone and Reichlin (2011)[11] and Marta Banbura, Domenico Giannone, Michele Modugno & Lucrezia Reichlin (2013) [12] provide surveys of the basic methods and more recent refinements.

Nowcasting methods based on social media content (such as Twitter) have been developed to estimate hidden quantities such as the 'mood' of a population or the presence of a flu epidemic.[13][14]

A simple to implement regression-based approach to nowcasting involves mixed-data sampling or MIDAS regressions (see Andreou, Ghysels and Kourtellos (2011)[15]). Mixed-data sampling (MIDAS) is an econometric regression or filtering method developed by Ghysels et al. There is now a substantial literature on MIDAS regressions and their applications, including Andreou et al. (2010),[16] and especially Andreou et al. (2013).[17]

The regression models can be viewed in some cases as substitutes for the Kalman filter when applied in the context of mixed frequency data. Bai, Ghysels and Wright (2013),[18] examine the relationship between MIDAS regressions and Kalman filter state space models applied to mixed frequency data. In general, the latter involve a system of equations, whereas in contrast MIDAS regressions involve a (reduced form) single equation. As a consequence, MIDAS regressions might be less efficient, but also less prone to specification errors. In cases where the MIDAS regression is only an approximation, the approximation errors tend to be small.


  1. ^
  2. ^ Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
  3. ^ Maximo Camacho & Gabriel Perez-Quiros, 2010. "Introducing the euro-sting: Short-term indicator of euro area growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 663-694.
  4. ^ Matheson, Troy D., 2010. "An analysis of the informational content of New Zealand data releases: The importance of business opinion surveys," Economic Modelling, Elsevier, vol. 27(1), pages 304-314, January.
  5. ^ Martin D. D. Evans, 2005. "Where Are We Now? Real-Time Estimates of the Macroeconomy," International Journal of Central Banking, International Journal of Central Banking, vol. 1(2), September.
  6. ^ G. Rünstler & K. Barhoumi & S. Benk & R. Cristadoro & A. Den Reijer & A. Jakaitiene & P. Jelonek & A. Rua & K. Ruth & C. Van Nieuwenhuyze, 2009. "Short-term forecasting of GDP using large datasets: a pseudo real-time forecast evaluation exercise," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 595-611.
  7. ^ Elena Angelini & Marta Banbura & Gerhard Rünstler, 2010. "Estimating and forecasting the euro area monthly national accounts from a dynamic factor model," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing,CIRET, vol. 2010(1), pages 7.
  8. ^ Giannone, Domenico & Lucrezia Reichlin & Saverio Simonelli) "Is the UK still in recession?," Vox, 23 November 2009.
  9. ^ Lahiri, Kajal & Monokroussos, George, 2013."Nowcasting US GDP: The role of ISM business surveys," International Journal of Forecasting, Elsevier, vol. 29(4), pages 644-658.
  10. ^ Antolin-Diaz, Juan & Drechsel, Thomas & Petrella, Ivan, 2014. "Following the Trend: Tracking GDP when Long-Run Growth is Uncertain," CEPR Discussion Papers 10272, C.E.P.R. Discussion Papers.
  11. ^ Marta Banbura, Domenico Giannone & Lucrezia Reichlin, 2010 ‘Nowcasting’, Working Papers ECARES 2010-021, ULB – Universite Libre de Bruxelles, in Michael P. Clements and David F. Hendry, editors, Oxford Handbook on Economic Forecasting
  12. ^ Marta Banbura, Domenico Giannone, Michele Modugno & Lucrezia Reichlin, 2013 'Nowcasting and the Real-Time Dataflow', Handbook on Economic Forecasting, eds. G. Elliot and A. Timmerman, Elsevier
  13. ^ Nowcasting the mood of the nation, Thomas Lansdall‐Welfare, Vasileios Lampos, Nello Cristianini, Significance, Volume 9 Issue 4 (August 2012)
  14. ^ Nowcasting Events from the Social Web with Statistical Learning, Vasileios Lampos and Nello Cristianini, ACM TIST 3(4), n.72, 2012.
  15. ^ Andreou, Elena & Eric Ghysels & Andros Kourtellos "Forecasting with Mixed-Frequency Data", Oxford Handbook of Economic Forecasting, Michael P. Clements and David F. Hendry (ed.) Chapter 8.
  16. ^ Andreou, Elena & Eric Ghysels & Andros Kourtellos "Regression Models with Mixed Sampling Frequencies", Journal of Econometrics, 158, 246-261.
  17. ^ Andreou, Elena & Eric Ghysels & Andros Kourtellos "Should macroeconomic forecasters use daily financial data and how?", Journal of Business and Economic Statistics 31, 240-251.
  18. ^ Bai, Jennie, Eric Ghysels and Jonathan Wright "State Space Models and MIDAS Regressions" Econometric Reviews, 32, 779–813

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