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A Spectral EM Algorithm for Dynamic Factor Models

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We introduce a frequency domain version of the EM algorithm for general dynamic factor models. We consider both AR and ARMA processes, for which we develop iterative indirect inference procedures analogous to the algorithms in Hannan (1969). Although our proposed procedure allows researchers to estimate such models by maximum likelihood with many series even without good initial values, we recommend switching to a gradient method that uses the EM principle to swiftly compute frequency domain analytical scores near the optimum. We successfully employ our algorithm to construct an index that captures the common movements of US sectoral employment growth rates.

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Paper provided by CEMFI in its series Working Papers with number wp2014_1411.

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Date of creation: Dec 2014
Handle: RePEc:cmf:wpaper:wp2014_1411
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  1. Narasimhan Jegadeesh & George Pennacchi, 1996. "The behavior of interest rates implied by the term structure of Eurodollar future," Proceedings, Federal Reserve Bank of Cleveland, issue Aug, pages 426-451.
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  3. Abraham, Katharine G & Katz, Lawrence F, 1986. "Cyclical Unemployment: Sectoral Shifts or Aggregate Disturbances?," Journal of Political Economy, University of Chicago Press, vol. 94(3), pages 507-522, June.
  4. Ruud, Paul A., 1991. "Extensions of estimation methods using the EM algorithm," Journal of Econometrics, Elsevier, vol. 49(3), pages 305-341, September.
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  8. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1014-1024, November.
  9. Rogerson, Richard, 1987. "An Equilibrium Model of Sectoral Reallocation," Journal of Political Economy, University of Chicago Press, vol. 95(4), pages 824-834, August.
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  13. Demos, Antonis & Sentana, Enrique, 1998. "An EM Algorithm for Conditionally Heteroscedastic Factor Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 357-361, July.
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  19. Gabriele Fiorentini & Enrique Sentana, 2016. "Neglected serial correlation tests in UCARIMA models," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(1), pages 121-178, March.
  20. Watson, Mark W. & Kraft, Dennis F., 1984. "Testing the interpretation of indices in a macroeconomic index model," Journal of Monetary Economics, Elsevier, vol. 13(2), pages 165-181, March.
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  24. James H. Stock & Mark W. Watson, 1993. "A Procedure for Predicting Recessions with Leading Indicators: Econometric Issues and Recent Experience," NBER Chapters,in: Business Cycles, Indicators and Forecasting, pages 95-156 National Bureau of Economic Research, Inc.
  25. repec:adr:anecst:y:2000:i:58:p:01 is not listed on IDEAS
  26. Diebold, Francis X. & Rudebusch, Glenn D. & Borag[caron]an Aruoba, S., 2006. "The macroeconomy and the yield curve: a dynamic latent factor approach," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 309-338.
  27. Chris Heaton & Victor Solo, 2004. "Identification of causal factor models of stationary time series," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 618-627, December.
  28. Hannan, E J & Nicholls, D F, 1972. "The Estimation of Mixed Regression, Autoregression, Moving Average, and Distributed Lag Models," Econometrica, Econometric Society, vol. 40(3), pages 529-547, May.
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