Nowcasting (economics)

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Nowcasting in economics is the prediction of the very recent past, the present, and the very near future state of an economic indicator. The term is a portmanteau of "now" and "forecasting" and originates in meteorology. Typical measures used to assess the state of an economy, such as gross domestic product (GDP) or inflation, are only determined after a delay and are subject to revision. [1] In these cases, nowcasting such indicators can provide an estimate of the variables before the true data are known. Nowcasting models have been applied most notably in Central Banks, who use the estimates to monitor the state of the economy in real-time as a proxy for official measures. [2] [3]

Contents

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. Many official measures are not timely due to the difficulty in collecting information. Historically, nowcasting techniques have been based on simplified heuristic approaches but now rely on complex econometric techniques. Using these statistical models to produce predictions eliminates the need for informal judgement. [4]

Nowcast models can exploit information from a large quantity of data series at different frequencies and with different publication lags. [5] 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 the official estimate of GDP 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. [6] [7] [8] [9] [10] [11] [12] [13]

Banbura, Giannone and Reichlin (2011) [14] and Marta Banbura, Domenico Giannone, Michele Modugno & Lucrezia Reichlin (2013) [15] 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 sentiment such as the 'mood' of a population [16] or the presence of a flu epidemic. [17]

A simple-to-implement, regression-based approach to nowcasting involves mixed-data sampling or MIDAS regressions. [18] The MIDAS regressions can also be combined with machine learning approaches. [19]

Econometric models can improve accuracy. [20] Such models can be built using bayesian vector autoregressions, dynamic factors, bridge equations using time series methods, or some combination with other methods. [21]

Implementation

Economic nowcasting is largely developed by and used in central banks to support monetary policy.

Many of the Reserve Banks of the US Federal Reserve System publish macroeconomic nowcasts. The Federal Reserve Bank of Atlanta publishes GDPNow to track GDP. [3] [21] Similarly, the Federal Reserve Bank of New York publishes a dynamic factor model nowcast. [2] Neither are official forecasts of the Federal Reserve regional bank, system, or the FOMC; nor do they incorporate human judgment.

Nowcasting can also be used to estimate inflation [22] or the business cycle. An example of a business cycle nowcast is the ADS Index. [23]

Related Research Articles

Econometrics is an application of statistical methods to economic data in order to give empirical content to economic relationships. More precisely, it is "the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference". An introductory economics textbook describes econometrics as allowing economists "to sift through mountains of data to extract simple relationships". Jan Tinbergen is one of the two founding fathers of econometrics. The other, Ragnar Frisch, also coined the term in the sense in which it is used today.

<span class="mw-page-title-main">Recession</span> Business cycle contraction

In economics, a recession is a business cycle contraction that occurs when there is a general decline in economic activity. Recessions generally occur when there is a widespread drop in spending. This may be triggered by various events, such as a financial crisis, an external trade shock, an adverse supply shock, the bursting of an economic bubble, or a large-scale anthropogenic or natural disaster.

<span class="mw-page-title-main">Business cycle</span> Intervals of expansion and recession in economic activity

Business cycles are intervals of expansion followed by recession in economic activity. A recession is sometimes technically defined as 2 quarters of negative GDP growth, but definitions vary; for example, in the United States, a recession is defined as "a significant decline in economic activity spread across the market, lasting more than a few months, normally visible in real GDP, real income, employment, industrial production, and wholesale-retail sales." The changes in economic activity that characterize business cycles have implications for the welfare of the broad population as well as for private institutions. Typically business cycles are measured by examining trends in a broad economic indicator such as Real Gross Domestic Production.

Economic forecasting is the process of making predictions about the economy. Forecasts can be carried out at a high level of aggregation—for example for GDP, inflation, unemployment or the fiscal deficit—or at a more disaggregated level, for specific sectors of the economy or even specific firms. Economic forecasting is a measure to find out the future prosperity of a pattern of investment and is the key activity in economic analysis. Many institutions engage in economic forecasting: national governments, banks and central banks, consultants and private sector entities such as think-tanks, companies and international organizations such as the International Monetary Fund, World Bank and the OECD. A broad range of forecasts are collected and compiled by "Consensus Economics". Some forecasts are produced annually, but many are updated more frequently.

Econometric models are statistical models used in econometrics. An econometric model specifies the statistical relationship that is believed to hold between the various economic quantities pertaining to a particular economic phenomenon. An econometric model can be derived from a deterministic economic model by allowing for uncertainty, or from an economic model which itself is stochastic. However, it is also possible to use econometric models that are not tied to any specific economic theory.

Econometric models involving data sampled at different frequencies are of general interest. Mixed-data sampling (MIDAS) is an econometric regression developed by Eric Ghysels with several co-authors. There is now a substantial literature on MIDAS regressions and their applications, including Ghysels, Santa-Clara and Valkanov (2006), Ghysels, Sinko and Valkanov, Andreou, Ghysels and Kourtellos (2010) and Andreou, Ghysels and Kourtellos (2013).

RATS, an abbreviation of Regression Analysis of Time Series, is a statistical package for time series analysis and econometrics. RATS is developed and sold by Estima, Inc., located in Evanston, IL.

<span class="mw-page-title-main">Eric Ghysels</span> Belgian economist (born 1956)

Eric Ghysels is a Belgian economist with interest in finance and time series econometrics, and in particular the fields of financial econometrics and financial technology. He is the Edward M. Bernstein Distinguished Professor of Economics at the University of North Carolina and a Professor of Finance at the Kenan-Flagler Business School. He is also the Faculty Research Director of the Rethinc.Labs at the Frank Hawkins Kenan Institute of Private Enterprise.

<span class="mw-page-title-main">Great Moderation</span> Phenomenon in economies of developed nations since the mid-1980s

The Great Moderation is a period in the United States of America starting from the mid-1980s until at least 2007 characterized by the reduction in the volatility of business cycle fluctuations in developed nations compared with the decades before. It is believed to be caused by institutional and structural changes, particularly in central bank policies, in the second half of the twentieth century.

The Survey of Professional Forecasters (SPF) is a quarterly survey of macroeconomic forecasts for the economy of the United States issued by the Federal Reserve Bank of Philadelphia. It is the oldest such survey in the United States.

In statistics and econometrics, Bayesian vector autoregression (BVAR) uses Bayesian methods to estimate a vector autoregression (VAR) model. BVAR differs with standard VAR models in that the model parameters are treated as random variables, with prior probabilities, rather than fixed values.

Used in a number of sciences, ranging from econometrics to meteorology, consensus forecasts are predictions of the future that are created by combining several separate forecasts which have often been created using different methodologies. Also known as combining forecasts, forecast averaging or model averaging and committee machines, ensemble averaging or expert aggregation. Applications can range from forecasting the weather to predicting the annual Gross Domestic Product of a country or the number of cars a company or an individual dealer is likely to sell in a year. While forecasts are often made for future values of a time series, they can also be for one-off events such as the outcome of a presidential election or a football match.

Kenneth David West is the John D. MacArthur and Ragnar Frisch Professor of Economics in the Department of Economics at the University of Wisconsin. He is currently co-editor of the Journal of Money, Credit and Banking, and has previously served as co-editor of the American Economic Review. He has published widely in the fields of macroeconomics, finance, international economics and econometrics. Among his honors are the John M. Stauffer National Fellowship in Public Policy at the Hoover Institution, Alfred P. Sloan Research Fellowship, Fellow of the Econometric Society, and Abe Fellowship. He has been a research associate at the NBER since 1985.

The Greenbook of the Federal Reserve Board of Governors is a book with projections of various economic indicators for the economy of the United States produced by the Federal Reserve Board before each meeting of the Federal Open Market Committee. The projections are referred to as Greenbook projections or Greenbook forecasts. Many of the variables projected coincide with variables covered in the Survey of Professional Forecasters.

<span class="mw-page-title-main">Lucrezia Reichlin</span> Italian economist

Lucrezia Reichlin is an Italian economist who has been a professor at London Business School since 2008.

Bayesian structural time series (BSTS) model is a statistical technique used for feature selection, time series forecasting, nowcasting, inferring causal impact and other applications. The model is designed to work with time series data.

The Barnett critique, named for the work of William A. Barnett in monetary economics, argues that internal inconsistency between the aggregation theory used to produce monetary aggregates and the economic theory used to produce the models within which the aggregates are used are responsible for the appearance of unstable demand and supply for money. The Barnett critique has produced a long and growing literature on monetary aggregation and index number theory and the use of the resulting aggregates in econometric modeling and monetary policy.

Francis X. Diebold is an American economist known for his work in predictive econometric modeling, financial econometrics, and macroeconometrics. He earned both his B.S. and Ph.D. degrees at the University of Pennsylvania, where his doctoral committee included Marc Nerlove, Lawrence Klein, and Peter Pauly. He has spent most of his career at Penn, where he has mentored approximately 75 Ph.D. students. Presently he is Paul F. and Warren S. Miller Professor of Social Sciences and Professor of Economics at Penn’s School of Arts and Sciences, and Professor of Finance and Professor of Statistics at Penn’s Wharton School. He is also a Faculty Research Associate at the National Bureau of Economic Research in Cambridge, Massachusetts, and author of the No Hesitations blog.

The Aruoba-Diebold-Scotti Business Conditions Index is a coincident business cycle indicator used in macroeconomics in the United States. The index measures business activity, which may be correlated with periods of expansion and contraction in the economy. The primary and novel function of the ADS index stems from its use of high-frequency economic data and subsequent high-frequency updating, opposed to the traditionally highly-lagged and infrequently-published macroeconomic data such as GDP.

<span class="mw-page-title-main">Tobias Adrian</span> German and American economist

Tobias Adrian is a German and American economist who has been Financial Counsellor of the International Monetary Fund and Head of their Monetary and Capital Markets Department since 2017. He was previously employed at the Federal Reserve Bank of New York, where he was a Senior Vice President and the Associate Director of the Research and Statistics Group. His research covers aspects of risk to the wider economy of developments in capital markets. His work has covered the global financial crisis, monetary policy transmission, and the yield curve.

References

  1. ↑ Hueng, C. James (2020-08-25), "Alternative Economic Indicators", W.E. Upjohn Institute, pp. 1–4, doi: 10.17848/9780880996778.ch1 , ISBN   978-0-88099-677-8 {{citation}}: Missing or empty |title= (help)
  2. 1 2 "Nowcasting Report - FEDERAL RESERVE BANK of NEW YORK". www.newyorkfed.org. Retrieved 2020-09-24.
  3. 1 2 "GDPNow". www.frbatlanta.org. Retrieved 2020-09-24.
  4. ↑ 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.
  5. ↑ Bańbura, Marta; Modugno, Michele (2012-11-12). "Maximum Likelihood Estimation of Factor Models on Datasets with Arbitrary Pattern of Missing Data". Journal of Applied Econometrics. 29 (1): 133–160. doi:10.1002/jae.2306. hdl: 10419/153623 . ISSN   0883-7252. S2CID   14231301.
  6. ↑ 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.
  7. ↑ 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.
  8. ↑ 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.
  9. ↑ 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.
  10. ↑ 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.
  11. ↑ 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.
  12. ↑ 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. S2CID   12028550.
  13. ↑ 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.
  14. ↑ Banbura, Marta; Giannone, Domenico; Reichlin, Lucrezia (2010). "Nowcasting". In Clements, Michael P.; Hendry, David F. (eds.). Oxford Handbook on Economic Forecasting. Oxford University Press.
  15. ↑ 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. Vol. 2. Elsevier. pp. 195–237. doi:10.1016/B978-0-444-53683-9.00004-9. hdl:10419/153997. ISBN   9780444536839. S2CID   14278918.
  16. ↑ Lansdall‐Welfare, Thomas; Lampos, Vasileios; Cristianini, Nello (August 2012). "Nowcasting the mood of the nation". Significance. 9 (4): 26–28. doi: 10.1111/j.1740-9713.2012.00588.x . Archived from the original on 20 August 2012.
  17. ↑ 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. S2CID   8297993.
  18. ↑ Andreou, Elena; Ghysels, Eric; Kourtellos, Andros (2011-07-08). "Forecasting with Mixed-Frequency Data". Oxford Handbooks Online: 225–246. doi:10.1093/oxfordhb/9780195398649.013.0009. ISBN   978-0195398649.
  19. ↑ Babii, Andrii; Ghysels, Eric; Striaukas, Jonas (2020). "Machine learning time series regressions with an application to nowcasting".
  20. ↑ 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.
  21. 1 2 Higgins, Patrick (July 2014). "GDPNow: A Model for GDP "Nowcasting"" (PDF). Federal Reserve Bank of Atlanta Working Paper Series.
  22. ↑ Ahn, Hie Joo; Fulton, Chad (2020). "Index of Common Inflation Expectations". FEDS Notes. 2020 (2551). doi:10.17016/2380-7172.2551. ISSN   2380-7172. S2CID   225316591 – via Board of Governors of the Federal Reserve System.
  23. ↑ Aruoba, S. Boragan; Diebold, Francis; Scotti, Chiara (2008). "Real-Time Measurement of Business Conditions". Cambridge, MA. doi: 10.3386/w14349 .{{cite journal}}: Cite journal requires |journal= (help)
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