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An Integrated Model of the Data Measurement and Data Generation Processes with an Application to Consumers' Expenditure

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  • Patterson, K D

Abstract

An integrated model is defined as one that not only models the data generation process (DGP) but also models the data measurement process. A natural framework for such an integrated model is the state space approach, with the optimal combination of preliminary vintages of data and predictions from the data generation process model being obtained by application of the Kalman filter. The author shows that substantial reductions in the mean square error of preliminary vintages of data on consumers' expenditure can be obtained from this approach. This provides further evidence that preliminary vintages are not efficient forecasts of the final vintage. Copyright 1995 by Royal Economic Society.

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  • Patterson, K D, 1995. "An Integrated Model of the Data Measurement and Data Generation Processes with an Application to Consumers' Expenditure," Economic Journal, Royal Economic Society, vol. 105(428), pages 54-76, January.
    • Handle: RePEc:ecj:econjl:v:105:y:1995:i:428:p:54-76
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    Cited by:

    1. 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.
    2. 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.
    3. Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.
    4. 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.
    5. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    6. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
    7. Juan Manuel Julio Román, 2011. "Modeling Data Revisions," Borradores de Economia 7929, Banco de la Republica.
    8. Swanson, Norman R. & White, Halbert, 1997. "Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 439-461, December.
    9. Clements, Michael P & Galvão, Ana Beatriz, 2006. "Macroeconomic Forecasting with Mixed Frequency Data : Forecasting US output growth and inflation," The Warwick Economics Research Paper Series (TWERPS) 773, University of Warwick, Department of Economics.
    10. Carriero, Andrea & Clements, Michael P. & Galvão, Ana Beatriz, 2015. "Forecasting with Bayesian multivariate vintage-based VARs," International Journal of Forecasting, Elsevier, vol. 31(3), pages 757-768.
    11. Patterson, K. D., 1995. "Forecasting the final vintage of real personal disposable income: A state space approach," International Journal of Forecasting, Elsevier, vol. 11(3), pages 395-405, September.
    12. Clements, Michael P., 2006. "Internal consistency of survey respondentsíforecasts: Evidence based on the Survey of Professional Forecasters," Economic Research Papers 269742, University of Warwick - Department of Economics.
    13. 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.
    14. Patterson, K. D., 2003. "Exploiting information in vintages of time-series data," International Journal of Forecasting, Elsevier, vol. 19(2), pages 177-197.
    15. Haiyan Song & Peter Romilly & Xiaming Liu, 1998. "The UK consumption function and structural instability: improving forecasting performance using a time-varying parameter approach," Applied Economics, Taylor & Francis Journals, vol. 30(7), pages 975-983.
    16. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2013. "Forecasting by factors, by variables, by both or neither?," Journal of Econometrics, Elsevier, vol. 177(2), pages 305-319.
    17. Michael P Clements & Ana Beatriz Galvao, 2017. "Data Revisions and Real-time Probabilistic Forecasting of Macroeconomic Variables," ICMA Centre Discussion Papers in Finance icma-dp2017-01, Henley Business School, University of Reading.
    18. Sucharita Ghosh & Donald Lien, 2001. "Forecasting with preliminary data: a comparison of two methods," Applied Economics, Taylor & Francis Journals, vol. 33(6), pages 721-726.
    19. 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.

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