Showing posts with label real time. Show all posts
Showing posts with label real time. Show all posts

Monday, 30 June 2025

Dynamic Pricing

 


Dynamic pricing is a strategy where businesses adjust the prices of goods or services in real-time based on various market factors, such as demand, supply, competitor pricing, and even customer behavior. It's a flexible approach that allows companies to maximize revenue by setting prices at the highest level the market will bear at any given time. 



Here's a more detailed explanation:


How it works:
  • Dynamic pricing involves constantly monitoring market conditions and using data analysis and algorithms to determine optimal prices. 

  • When demand is high, prices increase, and when demand is low, prices decrease. 
  • This can be implemented in real-time or at set intervals, depending on the specific strategy. 


  • Factors considered include: 


    • Demand: High demand often leads to higher prices. 


    • Supply: Limited availability can also drive up prices.

    •  
    • Competitor pricing: Monitoring competitor prices helps businesses stay competitive.

    •  
    • Customer behavior: Understanding customer preferences and willingness to pay can inform pricing decisions.

    •  
    • Seasonality: Prices may be adjusted based on seasonal demand. 
    • Time of day: Certain times of day might see higher or lower prices. 
Examples:


  • Ride-hailing apps:
    Companies like Uber and Lyft use dynamic pricing, often referred to as surge pricing, to adjust fares based on real-time demand. 


  • Hotels:
    Hotel prices can vary based on occupancy rates, time of year, and other factors. 


  • E-commerce:
    Online retailers use dynamic pricing to adjust prices based on inventory levels, competitor pricing, and other market conditions.

  •  
  • Event tickets:
    Ticket prices for concerts, sporting events, and other attractions can fluctuate based on demand and availability.
  •  
Benefits:

  • Increased revenue:
    Dynamic pricing can help businesses maximize their revenue by capitalizing on periods of high demand.

  •  
  • Efficient inventory management:
    By adjusting prices based on supply, businesses can better manage inventory levels. 


  • Improved customer engagement:
    Personalized pricing and promotions can increase customer satisfaction and engagement. 


  • Competitive advantage:
    Dynamic pricing can help businesses stay competitive by reacting quickly to market changes. 
Potential concerns:


  • Customer perception: Some customers may perceive dynamic pricing as unfair or exploitative.

  •  
  • Transparency: It's important for businesses to be transparent about their dynamic pricing strategies.

  •  
  • Ethical considerations: The use of dynamic pricing raises ethical considerations, particularly regarding potential price discrimination. 



  • Google search listings forDynamic Pricing


  • Digital Prcing and Dynamic Pricing
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Monday, 3 April 2023

A book on Real Time.....

 

Economics in Real Time

A Theoretical Reconstruction
John McDermott
A new model for contemporary economic behavior

Description

This book offers a new model for contemporary economic behavior that accounts for changes since neoclassical and Marxian microeconomics were formulated over a century ago. By incorporating real time into the analysis of sales and purchases, the phenomena of product innovation, advertising and distribution, the provision of consumer credit, and, ultimately, the production of a changing workforce all become intrinsic to microeconomic analysis rather than being treated as extraneous to fundamental theory.

Economics in Real Time transforms the analysis of contemporary sales and purchases. In mainstream economics the series of purchases, say, of a personal computer, then of software upgrades, peripherals, on-line services, and even support services are analyzed as discrete, essentially unrelated transactions. However counterintuitive, this approach is theoretically necessary to sustain the free-market narrative, its price and general equilibrium theories, and its efficiency and welfare theorems. Economics in Real Time instead links such related purchases within what is called a "sale/purchase state" occupying the time interval that begins with the initial purchase of the PC and ends only when all of the PC's services have been exchanged to the buyer. Under this analysis, typical contemporary sale/purchase states, as for automobiles, benefit plans, and electronic goods, place the purchaser in continuing, often dependent relationships to multiple sellers, at least some of which were not even overt partners to the initial purchase. Moreover they typically impose a continuing stream of expenditures upon the purchaser, as for automobile upkeep or music CDs, and so forth.

Economics in Real Time analyzes a contemporary economy as shaped in both its narrowly economic and broadly social features by these sale/purchase states. It draws a radically different picture of its terrain, challenging at the most fundamental level both the relevance and the theoretical warrant of the free-market conception.

John McDermott is Professor Emeritus of the State University of New York and a member of the editorial board of the Review of Radical Political Economics. His books include Corporate Society: Class, Property, and Contemporary Capitalism. His work has appeared in the New York Review of Books, the Nation, and other venues. He now lives in the Boston area.

Praise / Awards

  • "John McDermott's thought-provoking and ambitious book challenges conventional notions of how market economies function and evolve. Drawing on Marx, Gramsci, and Schumpeter, McDermott presents a detailed and sociologically nuanced account of the market process—one that is at odds with the 'spontaneous order' hypothesis of Popper and Hayek. This book will spark much debate."
    —Gary Mongiovi, Co-editor of Review of Political Economy and Professor, St. John's University
  • "John McDermott has set himself the task of no less than revolutionizing how economists conceptualize "voluntary exchange" both in the sale of goods and services and more importantly in the labor market (or—as McDermott would insist—labor markets!). In my opinion he has succeeded admirably in raising the necessary questions and pointing us in the proper direction. It remains to be seen if the profession, which has never been very good at assimilating successful criticism (witness the neutering of the Keynesian Revolution!), will take it seriously. It should, and we who appreciate McDermott's achievement have a responsibility to make sure that happens."
    —Michael Meeropol, Western New England College
  • "John McDermott asks, simply, 'What happens when we take the realities of time seriously in economic theory?' The result is Economics in Real Time, a mosaic of surprising, powerful insights."
    —Robert Pollin, Professor of Economics and Co-Director, Political Economy Research Institute, University of Massachusetts-Amherst
  • ". . . offers a new and intellectually impressive paradigm to describe and encapsulize contemporary economic fluctuations that takes into account changes that have been observed since the neoclassical and Marxian microeconomic theories created over a century ago."
    —Library Bookwatch

  • "...this book is largely a serious (and, in my view, quite balanced) appraisal of neoclassical and Marxian theories, focusing on their treatments of time. It deserves a look on this basis alone."
    —Rethinking Marxism






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Wednesday, 15 February 2023

Nowcasting

 


From Wikipedia, the free encyclopedia

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 portmanteau 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 978-0-88099-677-8 {{citation}}: Missing 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 June2015.
  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. ^ 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. 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.

External links[edit]

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