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Estimating DSGE models with unknown data persistence

Author

Listed:
  • Gianluca Moretti

    (Bank of Italy)

  • Giulio Nicoletti

    (Bank of Italy)

Abstract

Recent empirical literature shows that key macro variables such as GDP and productivity display long memory dynamics. For DSGE models, we propose a �Generalized� Kalman Filter to deal effectively with this problem: our method connects to and innovates upon data-filtering techniques already used in the DSGE literature. We show our method produces more plausible estimates of the deep parameters as well as more accurate out-of-sample forecasts of macroeconomic data.

Suggested Citation

  • Gianluca Moretti & Giulio Nicoletti, 2010. "Estimating DSGE models with unknown data persistence," Temi di discussione (Economic working papers) 750, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:wptemi:td_750_10
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    References listed on IDEAS

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    1. Ellen R. McGrattan, 2010. "Measurement with minimal theory," Quarterly Review, Federal Reserve Bank of Minneapolis, issue July, pages 2-13.
    2. Altissimo, Filippo & Mojon, Benoit & Zaffaroni, Paolo, 2009. "Can aggregation explain the persistence of inflation?," Journal of Monetary Economics, Elsevier, vol. 56(2), pages 231-241, March.
    3. Abadir, Karim M. & Caggiano, Giovanni & Talmain, Gabriel, 2013. "Nelson–Plosser revisited: The ACF approach," Journal of Econometrics, Elsevier, vol. 175(1), pages 22-34.
    4. Ravenna, Federico, 2007. "Vector autoregressions and reduced form representations of DSGE models," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2048-2064, October.
    5. Yongsung Chang & Taeyoung Doh & Frank Schorfheide, 2007. "Non‐stationary Hours in a DSGE Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(6), pages 1357-1373, September.
    6. Gil-Alana, Luis Alberiko & Moreno, Antonio, 2009. "Technology Shocks And Hours Worked: A Fractional Integration Perspective," Macroeconomic Dynamics, Cambridge University Press, vol. 13(5), pages 580-604, November.
    7. Christiano, Lawrence J. & Vigfusson, Robert J., 2003. "Maximum likelihood in the frequency domain: the importance of time-to-plan," Journal of Monetary Economics, Elsevier, vol. 50(4), pages 789-815, May.
    8. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2009. "New Keynesian Models: Not Yet Useful for Policy Analysis," American Economic Journal: Macroeconomics, American Economic Association, vol. 1(1), pages 242-266, January.
    9. Ruge-Murcia, Francisco J., 2007. "Methods to estimate dynamic stochastic general equilibrium models," Journal of Economic Dynamics and Control, Elsevier, vol. 31(8), pages 2599-2636, August.
    10. Ireland, Peter N., 2004. "A method for taking models to the data," Journal of Economic Dynamics and Control, Elsevier, vol. 28(6), pages 1205-1226, March.
    11. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    12. Joseph G. Haubrich & Andrew W. Lo, 2001. "The sources and nature of long-term memory in aggregate output," Economic Review, Federal Reserve Bank of Cleveland, issue Q II, pages 15-30.
    13. Karim Abadir & Gabriel Talmain, 2002. "Aggregation, Persistence and Volatility in a Macro Model," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 69(4), pages 749-779.
    14. Fabio Canova & Filippo Ferroni, 2011. "Multiple filtering devices for the estimation of cyclical DSGE models," Quantitative Economics, Econometric Society, vol. 2(1), pages 73-98, March.
    15. Gorodnichenko, Yuriy & Ng, Serena, 2010. "Estimation of DSGE models when the data are persistent," Journal of Monetary Economics, Elsevier, vol. 57(3), pages 325-340, April.
    16. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    17. Timothy Cogley & Argia M. Sbordone, 2008. "Trend Inflation, Indexation, and Inflation Persistence in the New Keynesian Phillips Curve," American Economic Review, American Economic Association, vol. 98(5), pages 2101-2126, December.
    18. María Dolores Gadea & Laura Mayoral, 2006. "The Persistence of Inflation in OECD Countries: A Fractionally Integrated Approach," International Journal of Central Banking, International Journal of Central Banking, vol. 2(1), March.
    19. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    20. Kydland, Finn E & Prescott, Edward C, 1982. "Time to Build and Aggregate Fluctuations," Econometrica, Econometric Society, vol. 50(6), pages 1345-1370, November.
    21. Rotemberg, Julio J & Woodford, Michael, 1996. "Real-Business-Cycle Models and the Forecastable Movements in Output, Hours, and Consumption," American Economic Review, American Economic Association, vol. 86(1), pages 71-89, March.
    22. Klein, Paul, 2000. "Using the generalized Schur form to solve a multivariate linear rational expectations model," Journal of Economic Dynamics and Control, Elsevier, vol. 24(10), pages 1405-1423, September.
    23. Gianluca Moretti, 2007. "Detecting long memory co-movements in macroeconomic time series," Temi di discussione (Economic working papers) 642, Bank of Italy, Economic Research and International Relations Area.
    24. Diebold, Francis X. & Rudebusch, Glenn D., 1989. "Long memory and persistence in aggregate output," Journal of Monetary Economics, Elsevier, vol. 24(2), pages 189-209, September.
    25. Sowell, Fallaw, 1992. "Modeling long-run behavior with the fractional ARIMA model," Journal of Monetary Economics, Elsevier, vol. 29(2), pages 277-302, April.
    26. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
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    Cited by:

    1. Raffaella Giacomini, 2015. "Economic theory and forecasting: lessons from the literature," Econometrics Journal, Royal Economic Society, vol. 18(2), pages 22-41, June.

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    More about this item

    Keywords

    DSGE models; long memory; Kalman Filter.;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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