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Preliminary data and econometric forecasting: an application with the Bank of Italy Quarterly Model

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  • Fabio Busetti

    (Bank of Italy, Research Department, Italy)

Abstract

This paper discusses the use of preliminary data in econometric forecasting. The standard practice is to ignore the distinction between preliminary and final data, the forecasts that do so here being termed na�ve forecasts. It is shown that in dynamic models a multistep-ahead na�ve forecast can achieve a lower mean square error than a single-step-ahead one, as it is less affected by the measurement noise embedded in the preliminary observations. The minimum mean square error forecasts are obtained by optimally combining the information provided by the model and the new information contained in the preliminary data, which can be done within the state space framework as suggested in numerous papers. Here two simple, in general suboptimal, methods of combining the two sources of information are considered: modifying the forecast initial conditions by means of standard regressions and using intercept corrections. The issues are explored using Italian national accounts data and the Bank of Italy Quarterly Econometric Model. Copyright © 2006 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.973
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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 25 (2006)
Issue (Month): 1 ()
Pages: 1-23

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Handle: RePEc:jof:jforec:v:25:y:2006:i:1:p:1-23

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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Cited by:
  1. 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.
  2. Juan Manuel Julio Román, 2011. "Modeling Data Revisions," BORRADORES DE ECONOMIA 007929, BANCO DE LA REPÚBLICA.
  3. George Kapetanios & Tony Yates, 2004. "Estimating Time-Variation in Measurement Error from Data Revisions: An Application to Forecasting in Dynamic Models," Working Papers 520, Queen Mary, University of London, School of Economics and Finance.
  4. Bouwman, Kees E. & Jacobs, Jan P.A.M., 2005. "Forecasting with real-time macroeconomic data: the ragged-edge problem and revisions," CCSO Working Papers 200505, University of Groningen, CCSO Centre for Economic Research.
  5. Richard Harrison & George Kapetanios & Tony Yates, 2004. "Forecasting with Measurement Errors in Dynamic Models," Working Papers 521, Queen Mary, University of London, School of Economics and Finance.
  6. Roberto Golinelli & Giuseppe Parigi, 2013. "Tracking world trade and GDP in real time," Temi di discussione (Economic working papers) 920, Bank of Italy, Economic Research and International Relations Area.

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