Showing posts with label economics. Show all posts
Showing posts with label economics. Show all posts

Wednesday, 16 March 2022

Category Economics from the P2P Foundation......

 * See also for more detailed introductory material: Introduction to the P2P Foundation Wiki Material about Economics

Economics is a vast field, (see e.g. Wikipedia: Economics) that has been based on premises that are no longer compatible with the very survival of the planet. The P2P Foundation therefore aims for a re-foundation of economics on different axioms, which we call Commons Economics;

See also below our list of related subdomains that are of particular interest to us and which represent alternatives to mainstream economics.

This category therefore, is for our general material on economics.

Key Quote

Tim Jackson: (2016)

"I have come to believe that building an economy that works is a precise, definable, pragmatic and meaningful task. Enterprise as service, work as participation, investment as a commitment to the future and money as a social good: these four principles provide the foundations for a profound and much-needed transformation of society." [1]

Key Concepts of This Category

Wikipedia suggests that Economics is "the social science that studies the production, distribution, and consumption of goods and services." It is centered around the basic concept of scarcity, and the market opportunities that such scarcity creates, and hardly takes into account what it considers as 'externalities', i.e. our planet and the other living beings on which we depend.

The P2P Foundation proposes an alternative economics that is centered around the institution of the Commons, systematically looks for natural and renewable Abundance, and seeks to develop win-win protocols supported by a generative market function and enabling institutions for the common good (the Partner State). We call this process of tranformation, the Commons Transition. Articles in this wiki therefore tend to view established economic theory through the critical perspective of Commons and P2P. We see economic practices not as static but as evolving over time, see Kojin Karatani's work on the Evolution of the Modes of Exchange . In particular, we seen an evolution from the mere view of active consumer, i.e. the Prosumer, to the active Produser, who both produces and uses. I.e. what we call 'commoners' or 'peer producers'.

Useful learning resources

Introductory

Deeper Study


P2P Foundation Material

  • Report: Value in the Commons Economy: Developments in Open and Contributory Value Accounting. By Michel Bauwens and Vasilis Niaros. Heinrich Boll Foundation, 2016. [2]
  • The Thermodynamic Efficiencies of Peer Production: research project of the P2P Foundation, in collaboration and under the leadership of the Blaqswan's Collective and others. See the report: PEER TO PEER AND THE COMMONS: A MATTER, ENERGY AND THERMODYNAMIC PERSPECTIVE. By Xavier Rizos and Celine Piques. [3]
  • P2P Accounting for Planetary Survival: Towards a P2P Infrastructure for a Socially Just Circular Society. By Michel Bauwens and Alex Pazaitis. Foreword by Kate Raworth. P2P Foundation, June 2019. [4]

Related Categories

(See also below for our sub-categories)

Subcategories

This category has the following 8 subcategories, out of 8 total.

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  • ► Money‎ (1,483 P, 1 F)

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Pages in category "Economics"

The following 200 pages are in this category, out of 1,318 total.

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(next page) This should take one to the rest of this extraordinary collection data on economics compiled by Michel Bauwens whose p2p foundation site is always worth a visit...The above was reproduced in an earlier post for this bog back in 2012 and since then a remarkable about of new material has been added! RS.,
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Monday, 8 November 2021

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.

External links[edit]

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