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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.

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Bibliographic Info

Paper provided by IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University in its series Working Papers with number 305.

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Date of creation: 2006
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Handle: RePEc:igi:igierp:305

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  1. Connor, Gregory & Korajczyk, Robert A, 1993. " A Test for the Number of Factors in an Approximate Factor Model," Journal of Finance, American Finance Association, American Finance Association, vol. 48(4), pages 1263-91, September.
  2. Stock, J.H. & Watson, M.W., 1989. "New Indexes Of Coincident And Leading Economic Indicators," Papers, Harvard - J.F. Kennedy School of Government 178d, Harvard - J.F. Kennedy School of Government.
  3. Danny Quah & Thomas J. Sargent, 1993. "A Dynamic Index Model for Large Cross Sections," NBER Chapters, National Bureau of Economic Research, Inc, in: Business Cycles, Indicators and Forecasting, pages 285-310 National Bureau of Economic Research, Inc.
  4. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, Econometric Society, vol. 70(1), pages 191-221, January.
  5. Chamberlain, Gary & Rothschild, Michael, 1983. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Econometrica, Econometric Society, Econometric Society, vol. 51(5), pages 1281-304, September.
  6. George Kapetanios & Massimiliano Marcellino, 2003. "A Comparison of Estimation Methods for Dynamic Factor Models of Large Dimensions," Working Papers, Queen Mary, University of London, School of Economics and Finance 489, Queen Mary, University of London, School of Economics and Finance.
  7. 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.
  8. Connor, Gregory & Korajczyk, Robert A., 1986. "Performance measurement with the arbitrage pricing theory : A new framework for analysis," Journal of Financial Economics, Elsevier, Elsevier, vol. 15(3), pages 373-394, March.
  9. 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, American Statistical Association, vol. 100, pages 830-840, September.
  10. Forni, Mario & Reichlin, Lucrezia, 1995. "Let's Get Real: A Dynamic Factor Analytical Approach to Disaggregated Business Cycle," CEPR Discussion Papers, C.E.P.R. Discussion Papers 1244, C.E.P.R. Discussion Papers.
  11. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 20(2), pages 147-62, April.
  12. Forni, Mario & Reichlin, Lucrezia, 1996. "Dynamic Common Factors in Large Cross-Sections," Empirical Economics, Springer, Springer, vol. 21(1), pages 27-42.
  13. Forni, Mario & Reichlin, Lucrezia, 1997. "National Policies and Local Economies: Europe and the United States," CEPR Discussion Papers, C.E.P.R. Discussion Papers 1632, C.E.P.R. Discussion Papers.
  14. 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., John Wiley & Sons, Ltd., vol. 20(5), pages 603-620.
  15. 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.
  16. Sin, Chor-Yiu & White, Halbert, 1996. "Information criteria for selecting possibly misspecified parametric models," Journal of Econometrics, Elsevier, Elsevier, vol. 71(1-2), pages 207-225.
  17. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2000. "Reference Cycles: The NBER Methodology Revisited," CEPR Discussion Papers, C.E.P.R. Discussion Papers 2400, C.E.P.R. Discussion Papers.
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Cited by:
  1. 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.
  2. Abo-Zaid, Salem, 2013. "Optimal monetary policy and downward nominal wage rigidity in frictional labor markets," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 37(1), pages 345-364.
  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," Working Paper Series, European Central Bank 1053, European Central Bank.
  4. Richard W P Holt, 2007. "Job Reallocation, Unemployment and Hours in a New Keynesian Model," ESE Discussion Papers, Edinburgh School of Economics, University of Edinburgh 172, Edinburgh School of Economics, University of Edinburgh.

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