Nowcasting (economics)

Nowcasting is the prediction of the present, the very near future and the very recent past in economics. The term is a contraction of "now" and "forecasting" and has been used for a long time in meteorology. It has recently become popular in economics as 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. Nowcasting models have been applied in many institutions, notably Central Banks, and the technique is used routinely to monitor the state of the economy in real time.

Principle

While weather forecasters know weather conditions today and only have to predict future weather, economists have to forecast the present and even the recent past. Historically, nowcasting techniques have been based on simplified heuristic approaches. A 2008 paper by Giannone, Reichlin and Small[1] finds 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 (such as jobless figures, industrial orders, trade balances) 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.

Development

Selected academic research papers show how this technique has developed.[2][3][4][5][6][7][8][9]

Banbura, Giannone and Reichlin (2011)[10] and Marta Banbura, Domenico Giannone, Michele Modugno & Lucrezia Reichlin (2013)[11] 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[12] or the presence of a flu epidemic.[13]

A simple to implement regression-based approach to nowcasting involves mixed-data sampling or MIDAS regressions (see Andreou, Ghysels and Kourtellos (2011)[14]).

Nowcasting can additionally be combined with econometric models to improve overall forecast accuracy and reduce errors.[15]

The Federal Reserve Bank of Atlanta publishes a nowcast for U.S. GDP called GDPNow.[16]

References
  1. Giannone, Domenico; Reichlin, Lucrezia; Small, David (May 2008). "Nowcasting: The real-time informational content of macroeconomic data". Journal of Monetary Economics. 55 (4): 665–676. CiteSeerX 10.1.1.597.705. doi:10.1016/j.jmoneco.2008.05.010. Retrieved 12 June 2015.
  2. Camacho, Maximo; Perez-Quiros, Gabriel (2010). "Introducing the euro-sting: Short-term indicator of euro area growth". Journal of Applied Econometrics. 25 (4): 663–694. doi:10.1002/jae.1174. Retrieved 12 June 2015.
  3. Matheson, Troy D. (January 2010). "An analysis of the informational content of New Zealand data releases: The importance of business opinion surveys". Economic Modelling. 27 (1): 304–314. doi:10.1016/j.econmod.2009.09.010. Retrieved 12 June 2015.
  4. Evans, Martin D. D. (September 2005). "Where Are We Now? Real-Time Estimates of the Macroeconomy". International Journal of Central Banking. 1 (2). Retrieved 12 June 2015.
  5. Rünstler, G.; Barhoumi, K.; Benk, S.; Cristadoro, R.; Den Reijer, A.; Jakaitiene, A.; Jelonek, P.; Rua, A.; Ruth, K.; Van Nieuwenhuyze, C. (2009). "Short-term forecasting of GDP using large datasets: a pseudo real-time forecast evaluation exercise". Journal of Forecasting. 28 (7): 595–611. doi:10.1002/for.1105.
  6. Angelini, Elena; Banbura, Marta; Rünstler, Gerhard (2010). "Estimating and forecasting the euro area monthly national accounts from a dynamic factor model". OECD Journal: Journal of Business Cycle Measurement and Analysis. 1: 7. Retrieved 12 June 2015.
  7. Domenico, Giannone; Reichlin, Lucrezia; Simonelli, Saverio (23 November 2009). "Is the UK still in recession? We don't think so". Vox. Retrieved 12 June 2015.
  8. Kajal, Lahiri; Monokroussos, George (2013). "Nowcasting US GDP: The role of ISM business surveys". International Journal of Forecasting. 29 (4): 644–658. CiteSeerX 10.1.1.228.3175. doi:10.1016/j.ijforecast.2012.02.010.
  9. Antolin-Diaz, Juan; Drechsel, Thomas; Petrella, Ivan (2014). "Following the Trend: Tracking GDP when Long-Run Growth is Uncertain". CEPR Discussion Papers 10272. Retrieved 12 June 2015.
  10. Banbura, Marta; Giannone, Domenico; Reichlin, Lucrezia (2010). "Nowcasting". In Clements, Michael P.; Hendry, David F. (eds.). Oxford Handbook on Economic Forecasting. Oxford University Press.
  11. Banbura, Marta; Giannone, Domenico; Modugno, Michele; Reichlin, Lucrezia (2013). "Chapter 4. Nowcasting and the Real-Time Dataflow". In Elliot, G.; Timmerman, A. (eds.). Handbook on Economic Forecasting. Handbook of Economic Forecasting. 2. Elsevier. pp. 195–237. doi:10.1016/B978-0-444-53683-9.00004-9. ISBN 9780444536839.
  12. Lansdall‐Welfare, Thomas; Lampos, Vasileios; Cristianini, Nello (August 2012). "Nowcasting the mood of the nation". Significance. 9 (4). Archived from the original on 20 August 2012.
  13. Lampos, Vasileios; Cristianini, Nello (2012). "Nowcasting Events from the Social Web with Statistical Learning" (PDF). ACM Transactions on Intelligent Systems and Technology. 3 (4): 1–22. doi:10.1145/2337542.2337557.
  14. 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.
  15. Tessier, Thomas H.; Armstrong, J. Scott (2015). "Decomposition of time-series by level and change". Journal of Business Research. 68 (8): 1755–1758. doi:10.1016/j.jbusres.2015.03.035.
  16. "GDPNow". www.frbatlanta.org. Retrieved 2018-09-08.
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