IDEAS home Printed from https://ideas.repec.org/p/wse/wpaper/68.html
   My bibliography  Save this paper

End of sample vs. real time data: perspectives for analysis of expectations

Author

Listed:
  • Emilia Tomczyk

    (Warsaw School of Economics)

Abstract

Data revision is defined as an adjustment published after the initial announcement had been made; it may reflect rectification of errors, availability of new information, etc. When economists use a database, they may not even be aware that some of the values have been revised, perhaps repeatedly, and corrected numbers may significantly differ from original ones. I propose to test whether including information on data revisions helps to model properties of expectations, improve quantification procedures, or adjust tests of rationality to data vintage. This paper presents review of literature and databases available for the purposes of real time analysis, and offers an introduction to empirical analysis of influence of data vintage on tests of expectations

Suggested Citation

  • Emilia Tomczyk, 2013. "End of sample vs. real time data: perspectives for analysis of expectations," Working Papers 68, Department of Applied Econometrics, Warsaw School of Economics.
  • Handle: RePEc:wse:wpaper:68
    as

    Download full text from publisher

    File URL: http://kolegia.sgh.waw.pl/pl/KAE/struktura/IE/struktura/ZES/Documents/Working_Papers/aewp03-13.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Evan F. Koenig & Sheila Dolmas & Jeremy Piger, 2003. "The Use and Abuse of Real-Time Data in Economic Forecasting," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 618-628, August.
    2. Jacopo Cimadomo, 2016. "Real-Time Data And Fiscal Policy Analysis: A Survey Of The Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 30(2), pages 302-326, April.
    3. S. Borağan Aruoba, 2008. "Data Revisions Are Not Well Behaved," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(2‐3), pages 319-340, March.
    4. Athanasios Orphanides & Simon van Norden, 2002. "The Unreliability of Output-Gap Estimates in Real Time," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 569-583, November.
    5. Yaari, Menahem E, 1987. "The Dual Theory of Choice under Risk," Econometrica, Econometric Society, vol. 55(1), pages 95-115, January.
    6. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
    7. Domenico Giannone & Jérôme Henry & Magdalena Lalik & Michele Modugno, 2012. "An Area-Wide Real-Time Database for the Euro Area," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1000-1013, November.
    8. Cox, James C. & Sadiraj, Vjollca, 2006. "Small- and large-stakes risk aversion: Implications of concavity calibration for decision theory," Games and Economic Behavior, Elsevier, vol. 56(1), pages 45-60, July.
    9. Hughes Hallett, Andrew & Bernoth, Kerstin & Lewis, John, 2008. "Did Fiscal Policy Makers Know What They Were Doing? Reassessing Fiscal Policy with Real Time Data," CEPR Discussion Papers 6758, C.E.P.R. Discussion Papers.
    10. Michał Lewandowski, 2013. "Risk Attitudes, Buying and Selling Price for a Lottery and Simple Strategies," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 5(1), pages 1-34, March.
    11. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    12. Athanasios Orphanides, 2001. "Monetary Policy Rules Based on Real-Time Data," American Economic Review, American Economic Association, vol. 91(4), pages 964-985, September.
    13. Mork, Knut Anton, 1987. "Ain't Behavin': Forecast Errors and Measurement Errors in Early GNP Estimates," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(2), pages 165-175, April.
    14. Palacios-Huerta, Ignacio & Serrano, Roberto, 2006. "Rejecting small gambles under expected utility," Economics Letters, Elsevier, vol. 91(2), pages 250-259, May.
    15. Phillips, Keith R. & Nordlund, James, 2012. "The efficiency of the benchmark revisions to the current employment statistics (CES) data," Economics Letters, Elsevier, vol. 115(3), pages 431-434.
    16. Ariel Rubinstein, 2006. "Lecture Notes in Microeconomic Theory," Online economics textbooks, SUNY-Oswego, Department of Economics, number gradmicro1.
    17. Harry Markowitz, 1952. "The Utility of Wealth," Journal of Political Economy, University of Chicago Press, vol. 60, pages 151-151.
    18. Stark, Tom & Croushore, Dean, 2002. "Forecasting with a real-time data set for macroeconomists," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 507-531, December.
    19. Jan P.A.M. Jacobs & Samad Sarferaz & Simon van Norden & Jan-Egbert Sturm, 2013. "Modeling Multivariate Data Revisions," CIRANO Working Papers 2013s-44, CIRANO.
    20. Croushore, Dean, 2006. "Forecasting with Real-Time Macroeconomic Data," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 17, pages 961-982, Elsevier.
    21. Harrison, Richard & Kapetanios, George & Yates, Tony, 2005. "Forecasting with measurement errors in dynamic models," International Journal of Forecasting, Elsevier, vol. 21(3), pages 595-607.
    22. Faust, Jon & Rogers, John H & Wright, Jonathan H, 2005. "News and Noise in G-7 GDP Announcements," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 403-419, June.
    23. Kontek, Krzysztof, 2009. "On Mental Transformations," MPRA Paper 16516, University Library of Munich, Germany.
    24. Vázquez, Jesús & María-Dolores, Ramón & Londoño, Juan M., 2012. "The Effect of Data Revisions on the Basic New Keynesian Model," International Review of Economics & Finance, Elsevier, vol. 24(C), pages 235-249.
    25. Patterson, K. D., 2003. "Exploiting information in vintages of time-series data," International Journal of Forecasting, Elsevier, vol. 19(2), pages 177-197.
    26. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    27. Jacopo Cimadomo, 2012. "Fiscal Policy in Real Time," Scandinavian Journal of Economics, Wiley Blackwell, vol. 114(2), pages 440-465, June.
    28. Stark, Tom & Croushore, Dean, 2002. "Reply to the comments on 'Forecasting with a real-time data set for macroeconomists'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 563-567, December.
    29. N. Gregory Mankiw & Matthew D. Shapiro, 1986. "News or Noise? An Analysis of GNP Revisions," NBER Working Papers 1939, National Bureau of Economic Research, Inc.
    30. Michael P. Clements & Ana Beatriz Galvão, 2011. "Improving Real-time Estimates of Output Gaps and Inflation Trends with Multiple-vintage Models," Working Papers 678, Queen Mary University of London, School of Economics and Finance.
    31. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
    32. Dean Croushore & Tom Stark, 2003. "A Real-Time Data Set for Macroeconomists: Does the Data Vintage Matter?," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 605-617, August.
    33. Jacobs, Jan P.A.M. & van Norden, Simon, 2011. "Modeling data revisions: Measurement error and dynamics of "true" values," Journal of Econometrics, Elsevier, vol. 161(2), pages 101-109, April.
    34. Michal Lewandowski, 2014. "Buying and selling price for risky lotteries and expected utility theory with gambling wealth," Journal of Risk and Uncertainty, Springer, vol. 48(3), pages 253-283, June.
    35. Golinelli, Roberto & Momigliano, Sandro, 2006. "Real-time determinants of fiscal policies in the euro area," Journal of Policy Modeling, Elsevier, vol. 28(9), pages 943-964, December.
    36. Krzysztof Kontek & Michal Lewandowski, 2013. "Range-Dependent Utility," Working Papers 69, Department of Applied Econometrics, Warsaw School of Economics.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
    2. Clements, Michael P. & Beatriz Galvao, Ana, 2010. "Real-time Forecasting of Inflation and Output Growth in the Presence of Data Revisions," Economic Research Papers 270771, University of Warwick - Department of Economics.
    3. Carlo Altavilla & Matteo Ciccarelli, 2011. "Monetary Policy Analysis in Real-Time. Vintage Combination from a Real-Time Dataset," CESifo Working Paper Series 3372, CESifo.
    4. Jacopo Cimadomo, 2016. "Real-Time Data And Fiscal Policy Analysis: A Survey Of The Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 30(2), pages 302-326, April.
    5. Krzysztof Kontek & Michal Lewandowski, 2013. "Range-Dependent Utility," Working Papers 69, Department of Applied Econometrics, Warsaw School of Economics.
    6. Clements, Michael P. & Galvão, Ana Beatriz, 2013. "Forecasting with vector autoregressive models of data vintages: US output growth and inflation," International Journal of Forecasting, Elsevier, vol. 29(4), pages 698-714.
    7. Clements, Michael P. & Beatriz Galvão, Ana, 2010. "First announcements and real economic activity," European Economic Review, Elsevier, vol. 54(6), pages 803-817, August.
    8. Clements Michael P., 2012. "Forecasting U.S. Output Growth with Non-Linear Models in the Presence of Data Uncertainty," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(1), pages 1-27, January.
    9. Michael P. Clements, 2014. "Anticipating Early Data Revisions to US GDP and the Effects of Releases on Equity Markets," ICMA Centre Discussion Papers in Finance icma-dp2014-06, Henley Business School, University of Reading.
    10. Marek RUSNAK, 2013. "Revisions to the Czech National Accounts: Properties and Predictability," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(3), pages 244-261, July.
    11. Michael P. Clements, 2017. "Assessing Macro Uncertainty in Real-Time When Data Are Subject To Revision," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 420-433, July.
    12. M. Mogliani & T. Ferrière, 2016. "Rationality of announcements, business cycle asymmetry, and predictability of revisions. The case of French GDP," Working papers 600, Banque de France.
    13. Jacobs, Jan P.A.M. & van Norden, Simon, 2011. "Modeling data revisions: Measurement error and dynamics of "true" values," Journal of Econometrics, Elsevier, vol. 161(2), pages 101-109, April.
    14. Cecilia Frale & Valentina Raponi, 2011. "Revisions in ocial data and forecasting," Working Papers LuissLab 1194, Dipartimento di Economia e Finanza, LUISS Guido Carli.
    15. Sinclair, Tara M., 2019. "Characteristics and implications of Chinese macroeconomic data revisions," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1108-1117.
    16. Denis Shibitov & Mariam Mamedli, 2021. "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series wps70, Bank of Russia.
    17. Clements, Michael P. & Galvao, Ana Beatriz, 2006. "Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US output growth and inflation," Economic Research Papers 269743, University of Warwick - Department of Economics.
    18. Casares, Miguel & Vázquez, Jesús, 2016. "Data Revisions In The Estimation Of Dsge Models," Macroeconomic Dynamics, Cambridge University Press, vol. 20(7), pages 1683-1716, October.
    19. Hecq, A.W. & Götz, T.B. & Urbain, J.R.Y.J., 2012. "Real-time forecast density combinations (forecasting US GDP growth using mixed-frequency data)," Research Memorandum 021, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    20. Krzysztof Kontek & Michal Lewandowski, 2018. "Range-Dependent Utility," Management Science, INFORMS, vol. 64(6), pages 2812-2832, June.

    More about this item

    Keywords

    end of sample (EOS) data; real time (RTV) data; data revisions; economic databases; expectations;
    All these keywords.

    JEL classification:

    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wse:wpaper:68. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Marcin Owczarczuk (email available below). General contact details of provider: https://edirc.repec.org/data/dxwawpl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.