Monday, 8 November 2021

Real-time Economy

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Real-time economy (not to be confused with real economy) is an environment where all the transactions between business entities are in digital format, increasingly automatically generated, and completed in real-time (as they occur) without store and forward processing, both from business and IT-processing perspectives. For enterprises, public sector, and citizens this means, for example, that (purchase) orders, order confirmations, invoices, and payments flow from system to system without delays. This makes it possible to move towards electronic archiving, electronic book-keeping, and automated accounting.[1]

Real-time economy can be described as an economic system from which the time-consuming intermediate steps between sales and reporting are eliminated. All the elements of business transaction, like sales, invoicing, accounting, tax payment and business reporting, will take place automatically, in a digital environment and in real time.[2]

For example, the real-time enterprise can be considered as a giant spreadsheet of sorts, in which new information, such as an order, is automatically processed and percolates through a firm's computer systems and those of its suppliers.[3] The core objective of the real-time economy is the reduction of latency between and within processes. Latency reduction will reduce capital occupancy costs by occupying assets (physical and labor) for less time.[4] It aims at promoting new technologies that enable a more real-time economy, processes, and services.[5]

Development of the concept[edit]

The term ‘real-time economy’ was first used already in 2002 Ludwig Siegele’s article “The Real-Time Economy: How about Now?”. This article illustrated how real-time business information was used for management purposes in the General Electric company.[6] The initial concept of Real-Time Economy was founded in collaboration with Tieto and Aalto University School of Business in 2006 and it is partly funded by the Finnish Funding Agency for Technology and Innovation.[2] A specialized Real-Time Economy Competence Center has been created for that matter in Aalto University. Several events and conferences have been organized by the competence center as from 2015.[7]

Estonia has a strong image of e-country and has also started to develop the concept since 2015 by the Estonian Association of ICT Enterprises within the context of Internet of Business projects in cooperation with Finnish counterparts.[8] Ever since, Estonia has assumed a leading role together with Finland in developing and implementing real-time economy principles in Nordic-Baltic cooperation. Three thematic round-table events have been organized in 2018-2019 both in Tallinn and Helsinki.[9] A relevant roadmap has been created for 2020-2027 under the guidance of the Estonian Ministry of Economic Affairs and Communications.[10] This subject has also been brought to the attention of the UNCTAD being one of the topics in 2020 eCommerce Week.[11]

Research[edit]

Numerous theses have been written on that subject matter in Aalto University since 2008, ranging from specifically real-time economy subject to adjacent subjects contributing to the ecosystem of real-time economy.[12]

In 2019 Tallinn University of Technology conducted an academic research on “Real-Time Economy: Definitions and Implementation Opportunities” covering literature, stakeholders’ views, overview of real-time economy initiatives in Europe as well as ideas for moving forward. It established that real-time economy is a digital ecosystem where transactions between diverse economic actors take place in or near real time. This means replacing paper-based business transactions and administrative procedures by automatic exchange of digital, structured and machine-readable data in standardized formats.[13]

A separate study on economic impact of real-time economy conducted by Tieto Estonia AS in 2020 established that real-time economy is not a specific type of economy, but a term that refers to an information-based infrastructure where data on economic transactions are transferred between the parties in real time. This study focused on economic and environmental impact of six real-time economy specific solutions: e-invoicing, e-receipt, e-CMR, implementing of XBRL GL in reporting, agricultural machinery data processing and real-time economic forecasting. It found that switching to real-time economy solutions in selected processes will save more than 210 million euros per year, over 14 million working hours per year and reduce greenhouse gas emissions in Estonia by over 27,000 tonnes per year.[14] This translates roughly to 150 euros per person, 10 working hours per person and about 20kg of CO2 per person in a year in any given country utilising real-time economy solutions.

Other applications and spill-over effects[edit]

Considering real-time economy concept’s ecosystemic nature and harnessing of real-time computing principle, the list of possible applications is virtually limitless. As mentioned above, the main goal of the real-time economy is the reduction of latency between and within processes while promoting new technologies that enable more real-time solutions and services. As a result, time and money can be saved by enterprises, citizens and governments if real-time economy principles are applied in everyday transaction of affairs.

Facilitator of circular economy[edit]

Real-time economy concept has all the necessary components to facilitate full realization of circular economy. Similarly to track-and-trace technology known from logistics (as well as drug distribution and tobacco products tracking) a like solution could be created for any product released on the market. It would be theoretically possible to track and trace any product’s life cycle from production facility up to recycling. Depending on the attributed information set for specific product an effective practice of circular economy could be realized.

Institution for green growth[edit]

Considering functional principles of real-time economy, it can be used as an institution in broad sense for enabling realization of green growth. Different building blocks of the real-time economy ecosystem can be used as an aggregate tool to facilitate the use and consumption of natural resources in a sustainable manner. In this regard, production data, logistics and distribution information could be captured from value chains and supply chains and attributed to specific products and services. Thus creating a new set of comparative advantage metrics for global trade and international competitiveness.

References[edit]

  1. ^ Real-Time Economy Community, realtimeeconomy.net
  2. ^ Jump up to:a b Gospodarka czasu rzeczywistego - biznesowy tygrysi skok, http://przegladbaltycki.pl/900,gospodarka-czasu-rzeczywistego-biznesowy-tygrysi-skok.html
  3. ^ The Real-Time Economy: How about Now? Ludwig Siegele - The Economist, February 1, 2002 http://www.cfo.com/article.cfm/3003286/1/c_2984786
  4. ^ The Real-time economy: The Technological Basis for Reengineered Business Reporting, Rutgers Accounting Web, http://raw.rutgers.edu/node/29
  5. ^ "Real-Time Economy". www.facebook.com. Retrieved 2020-12-26.
  6. ^ Roos, Christman (2020). "Reaalajamajandus – paberimajanduse ja tühitöö ajastu lõpp" (PDF). Riigikogu Toimetised. 41: 67–74, 211–212.
  7. ^ "Events Archive". Real-Time Economy Competence Center. Retrieved 2020-12-26.
  8. ^ "Creation of the IoB platform". ITL (in Estonian). Retrieved 2020-12-26.
  9. ^ "Üritused | Majandus- ja Kommunikatsiooniministeeriumi". www.mkm.ee (in Estonian). Retrieved 2020-12-26.
  10. ^ "Real-Time Economy Vision" (PDF).
  11. ^ "Real-Time Economy – a boost to e-commerce?". UNCTAD.
  12. ^ "Theses". Real-Time Economy Competence Center. Retrieved 2020-12-26.
  13. ^ Krimmer, Robert; Kadak, Tarmo; Alishani, Art; Toots, Maarja; Soe, Ralf-martin; Schmidt, Carsten (2019). Real-Time Economy: Definitions and Implementation Opportunities. Tallinn: Tallinn University of Technology.
  14. ^ "Reaalajamajanduse majandusliku mõju uuringu lõpparuanne" (PDF). 2020.


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Nowcasting

 From Wikipedia, the free encyclopedia/ 

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Nowcasting in economics is the prediction of the present, the very near future, and the very recent past state of an economic indicator. The term is a contraction of "now" and "forecasting" and originates in meteorology. It has recently become popular in economics as typical measures used to assess the state of an economy (e.g., gross domestic product (GDP)), are only determined after a long delay and are subject to revision.[1] 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]

Principle[edit]

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[edit]

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[edit]

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.[23]

References[edit]

  1. ^ Hueng, C. James (2020-08-25), "Alternative Economic Indicators", W.E. Upjohn Institute, pp. 1–4, doi:10.17848/9780880996778.ch1, ISBN 978-0-88099-677-8Missing or empty |title= (help)
  2. ^ Jump up to:a b "Nowcasting Report - FEDERAL RESERVE BANK of NEW YORK". www.newyorkfed.org. Retrieved 2020-09-24.
  3. ^ Jump up to:a b "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 June2015.
  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. 2. Elsevier. pp. 195–237. doi:10.1016/B978-0-444-53683-9.00004-9. 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. doi:10.1093/oxfordhb/9780195398649.013.0009.
  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. ^ Jump up to:a b 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 – 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.

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