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Real-Time Measurement of Business Conditions, Second Version

Author

Listed:
  • S. Boragan Aruoba

    () (Department of Economics, University of Maryland)

  • Francis X. Diebold

    () (Department of Economics, University of Pennsylvania and NBER)

  • Chiara Scotti

    () (Federal Reserve Board, Division of International Finance)

Abstract

We construct a framework for measuring economic activity at high frequency, potentially in real time. We use a variety of stock and flow data observed at mixed frequencies (including very high frequencies), and we use a dynamic factor model that permits exact filtering. We illustrate the framework in a prototype empirical example and a simulation study calibrated to the example.

Suggested Citation

  • 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.
  • Handle: RePEc:pen:papers:08-011
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    File URL: http://economics.sas.upenn.edu/system/files/working-papers/08-011.pdf
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    References listed on IDEAS

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    1. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
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    3. Martin D. D. Evans, 2005. "Where Are We Now? Real-Time Estimates of the Macroeconomy," International Journal of Central Banking, International Journal of Central Banking, vol. 1(2), September.
    4. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, pages 665-676.
    5. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, pages 169-194.
    6. Altissimo, Filippo & Bassanetti, Antonio & Cristadoro, Riccardo & Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia & Veronese, Giovanni, 2001. "EuroCOIN: A Real Time Coincident Indicator of the Euro Area Business Cycle," CEPR Discussion Papers 3108, C.E.P.R. Discussion Papers.
    7. Diebold, Francis X & Rudebusch, Glenn D, 1996. "Measuring Business Cycles: A Modern Perspective," The Review of Economics and Statistics, MIT Press, pages 67-77.
    8. Abeysinghe, Tilak, 2000. "Modeling variables of different frequencies," International Journal of Forecasting, Elsevier, pages 117-119.
    9. Maximo Camacho & Gabriel Perez-Quiros, 2008. "Introducing the EURO-STING: Short Term INdicator of Euro Area Growth," Working Papers 0807, Banco de España;Working Papers Homepage.
    10. Liu, H & Hall, Stephen G, 2001. "Creating High-Frequency National Accounts with State-Space Modelling: A Monte Carlo Experiment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(6), pages 441-449, September.
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    12. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, pages 540-554.
    13. Lucas, Robert E., 1977. "Understanding business cycles," Carnegie-Rochester Conference Series on Public Policy, Elsevier, pages 7-29.
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    Citations

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    Cited by:

    1. Andres Fernandez & Norman R. Swanson, 2009. "Real-time datasets really do make a difference: definitional change, data release, and forecasting," Working Papers 09-28, Federal Reserve Bank of Philadelphia.
    2. Franco, Ray John Gabriel & Mapa, Dennis S., 2014. "The Dynamics of Inflation and GDP Growth: A Mixed Frequency Model Approach," MPRA Paper 55858, University Library of Munich, Germany.
    3. Martina Hengge & Seton Leonard, 2017. "Factor Models for Non-Stationary Series: Estimates of Monthly U.S. GDP," IHEID Working Papers 13-2017, Economics Section, The Graduate Institute of International Studies.
    4. Francisco Blasques & Siem Jan Koopman & Max Mallee, 2014. "Low Frequency and Weighted Likelihood Solutions for Mixed Frequency Dynamic Factor Models," Tinbergen Institute Discussion Papers 14-105/III, Tinbergen Institute.

    More about this item

    Keywords

    Business cycle; Expansion; Recession; State space model; Macroeconomic forecasting; Dynamic factor model; Contraction; Turning point;

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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