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The Role of Search Frictions and Bargaining for Inflation Dynamics

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  • Massimiliano Marcellino
  • George Kapetanios

Abstract

The estimation of dynamic factor models for large sets of variables has attracted considerable attention recently, due to the increased availability of large datasets. In this paper we propose a new parametric methodology for estimating factors from large datasets based on state space models and discuss its theoretical properties. In particular, we show that it is possible to estimate consistently the factor space. We alsodevelop a consistent information criterion for the determination of the number of factors to be included in the model. Finally, we conduct a set of simulation experiments that show that our approach compares well with existing alternatives.

Suggested Citation

  • Massimiliano Marcellino & George Kapetanios, 2006. "The Role of Search Frictions and Bargaining for Inflation Dynamics," Working Papers 305, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  • Handle: RePEc:igi:igierp:305
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    References listed on IDEAS

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    1. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
    2. Forni, Mario & Reichlin, Lucrezia, 1996. "Dynamic Common Factors in Large Cross-Sections," Empirical Economics, Springer, vol. 21(1), pages 27-42.
    3. Danny Quah & Thomas J. Sargent, 1993. "A Dynamic Index Model for Large Cross Sections," NBER Chapters,in: Business Cycles, Indicators and Forecasting, pages 285-310 National Bureau of Economic Research, Inc.
    4. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2000. "Reference Cycles: The NBER Methodology Revisited," CEPR Discussion Papers 2400, C.E.P.R. Discussion Papers.
    5. Sin, Chor-Yiu & White, Halbert, 1996. "Information criteria for selecting possibly misspecified parametric models," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 207-225.
    6. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    7. George Kapetanios & Massimiliano Marcellino, 2003. "A Comparison of Estimation Methods for Dynamic Factor Models of Large Dimensions," Working Papers 489, Queen Mary University of London, School of Economics and Finance.
    8. Connor, Gregory & Korajczyk, Robert A, 1993. " A Test for the Number of Factors in an Approximate Factor Model," Journal of Finance, American Finance Association, vol. 48(4), pages 1263-1291, September.
    9. Chamberlain, Gary & Rothschild, Michael, 1983. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Econometrica, Econometric Society, vol. 51(5), pages 1281-1304, September.
    10. Gonzalo Camba-Mendez & George Kapetanios & Richard J. Smith & Martin R. Weale, 2001. "An automatic leading indicator of economic activity: forecasting GDP growth for European countries," Econometrics Journal, Royal Economic Society, vol. 4(1), pages 1-37.
    11. Forni, Mario & Reichlin, Lucrezia, 1997. "National Policies and Local Economies: Europe and the United States," CEPR Discussion Papers 1632, C.E.P.R. Discussion Papers.
    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, vol. 82(4), pages 540-554, November.
    13. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    14. Forni, Mario & Reichlin, Lucrezia, 1995. "Let's Get Real: A Dynamic Factor Analytical Approach to Disaggregated Business Cycle," CEPR Discussion Papers 1244, C.E.P.R. Discussion Papers.
    15. Massimiliano Marcellino & Carlo A. Favero & Francesca Neglia, 2005. "Principal components at work: the empirical analysis of monetary policy with large data sets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(5), pages 603-620.
    16. James H. Stock & Mark W. Watson, 1988. "A Probability Model of The Coincident Economic Indicators," NBER Working Papers 2772, National Bureau of Economic Research, Inc.
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    18. Connor, Gregory & Korajczyk, Robert A., 1986. "Performance measurement with the arbitrage pricing theory : A new framework for analysis," Journal of Financial Economics, Elsevier, vol. 15(3), pages 373-394, March.
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    Cited by:

    1. Abo-Zaid, Salem, 2013. "Optimal monetary policy and downward nominal wage rigidity in frictional labor markets," Journal of Economic Dynamics and Control, Elsevier, vol. 37(1), pages 345-364.
    2. Holt Richard, 2008. "Job Reallocation, Unemployment and Hours in a New Keynesian Model," The B.E. Journal of Macroeconomics, De Gruyter, vol. 8(1), pages 1-47, August.
    3. Christoffel, Kai & Costain, James & de Walque, Gregory & Kuester, Keith & Linzert, Tobias & Millard, Stephen & Pierrard, Olivier, 2009. "Inflation dynamics with labour market matching: assessing alternative specifications," Bank of England working papers 375, Bank of England.
    4. Burkhard Heer & Alfred Maussner, 2010. "Inflation and Output Dynamics in a Model with Labor Market Search and Capital Accumulation," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 13(3), pages 654-686, July.

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