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Creating High-Frequency National Accounts with State-Space Modelling: A Monte Carlo Experiment

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  • Liu, H
  • Hall, Stephen G

Abstract

This paper assesses a new technique for producing high-frequency data from lower frequency measurements subject to the full set of identities within the data all holding. The technique is assessed through a set of Monte Carlo experiments. The example used here is gross domestic product (GDP) which is observed at quarterly intervals in the United States and it is a flow economic variable rather than a stock. The problem of constructing an unobserved monthly GDP variable can be handled using state space modelling. The solution of the problem lies in finding a suitable state space representation. A Monte Carlo experiment is conducted to illustrate this concept and to identify which variant of the model gives the best monthly estimates. The results demonstrate that the more simple models do almost as well as more complex ones and hence there may be little gain in return for the extra work of using a complex model. Copyright © 2001 by John Wiley & Sons, Ltd.

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Bibliographic Info

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

Volume (Year): 20 (2001)
Issue (Month): 6 (September)
Pages: 441-49

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Handle: RePEc:jof:jforec:v:20:y:2001:i:6:p:441-49

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

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Cited by:
  1. S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2008. "Real-time measurement of business conditions," Working Papers 08-19, Federal Reserve Bank of Philadelphia.
  2. Pedregal, Diego J. & Pérez, Javier J., 2008. "Should quarterly government finance statistics be used for fiscal surveillane in Europe?," Working Paper Series 0937, European Central Bank.
  3. Martin D. D. Evans(Georgetown University and NBER), 2005. "Where Are We Now? Real-time Estimates of the Macro Economy," Working Papers gueconwpa~05-05-02, Georgetown University, Department of Economics.
  4. S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2007. "Real-Time Measurement of Business Conditions, Second Version," PIER Working Paper Archive 08-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 04 Apr 2008.
  5. Yasutomo Murasawa & Roberto S. Mariano, 2004. "Constructing a Coincident Index of Business Cycles Without Assuming a One-Factor Model," Econometric Society 2004 Far Eastern Meetings 710, Econometric Society.
  6. Pedregal, D.J. & Dejuán, O. & Gómez, N. & Tobarra, M.A., 2009. "Modelling demand for crude oil products in Spain," Energy Policy, Elsevier, vol. 37(11), pages 4417-4427, November.
  7. Byeongchan Seong & Sung K. Ahn & Peter Zadrozny, 2007. "Cointegration Analysis with Mixed-Frequency Data," CESifo Working Paper Series 1939, CESifo Group Munich.
  8. Nikolaus Hautsch & Fuyu Yang, 2014. "Bayesian Stochastic Search for the Best Predictors: Nowcasting GDP Growth," University of East Anglia Applied and Financial Economics Working Paper Series 056, School of Economics, University of East Anglia, Norwich, UK..

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