Measuring the Effects of a Shock to Monetary Policy - Humboldt ...

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Bayesian FAVARs with Agnostic Identification 65 [21] Del Negro, Marco and Christopher Otrok, 2004,Dynamic Factor Model with Time Varying Parameters,” Discussion paper, Federal Reserve Bank of Atlanta and Uni- versity of Virginia. [22] Favero, C.A. and M. Marcellino (2005) ” Large dataset, small models and monetary Policy in Europe”; CLM Economia , 249-269 [23] Favero, C.A., M. Marcellinho and F. Neglia (2002), ”Principal Components at Work: the empirical analysis of monetary policy with large datasets”; IGIER Working Paper No. 223 (Bocconi University), forthcoming, Journal of Applied Econometrics [24] Forni, M., M. Hallin, M. Lippi, and L. Reichlin (2000), ”The Generalized Dynamic Factor Model: Identification and Estimation”; Review of Economics and Statistics 82, pp. 540-54 [25] Forni, M. and L. Reichlin (1998), ”Dynamic Common Factors in Large Cross- Sections”; Empirical Economics 21, pp. 27-42 [26] Forni, M. and L. Reichlin (1998), ”Let’s Get Real: A Dynamic Factor Analytical Approach to Disaggregated Business Cycles”; Review of Economic Studies 65, pp. 453-74 [27] Gamerman, Dani (1997), ”Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference”; Chapman and Hall, New York [28] Gelman, A. and D.B.Rubin, (1992), ”A Single Sequence from the Gibbs Sampler Gives a False Sense of Security”; in J.M. Bernardo, J. O. Berger, A. P. Dawid, and A.F.M. Smith, eds., Bayesian Statistics, Oxford: University Press [29] Geman, S. and D. Geman, (1984), ”Stochastic Relaxation, Gibbs Distribution and the Bayesian Restoration of Images”; IEEE Transactions of Pattern Analysis and Machine Intelligence 6, pp. 721-41
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