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Aggregate Shocks and Macroeconomic Fluctuations: A Bayesian Approach

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  • Koop, G

Abstract

This paper describes Bayesian techniques for analysing the effects of aggregate shocks on macroeconomic time-series. Rather than calculate point estimates of the response of a time-series to an aggregate shock, we calculate the whole probability density function of the response and use Monte-Carlo or Gibbs sampling techniques to evaluate its properties. The proposed techniques impose identification restrictions in a way that includes the uncertainty in these restrictions, and thus are an improvement over traditional approaches that typically use least-squares techniques supplemented by bootstrapping. We apply these techniques in the context of two different models. A key finding is that measures of uncertainty, such as posterior standard deviations, are much larger than are their classical counterparts. Copyright 1992 by John Wiley & Sons, Ltd.

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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 7 (1992)
Issue (Month): 4 (Oct.-Dec.)
Pages: 395-411

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Handle: RePEc:jae:japmet:v:7:y:1992:i:4:p:395-411

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Cited by:
  1. Waggoner, Daniel F. & Zha, Tao, 2003. "A Gibbs sampler for structural vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 28(2), pages 349-366, November.
  2. Christopher A. Sims & Tao Zha, 1994. "Error Bands for Impulse Responses," Cowles Foundation Discussion Papers 1085, Cowles Foundation for Research in Economics, Yale University.
  3. Canova, Fabio & Ciccarelli, Matteo, 2001. "Forecasting and Turning Point Predictions in a Bayesian Panel VAR Model," CEPR Discussion Papers 2961, C.E.P.R. Discussion Papers.
  4. Koop, Gary & Korobilis, Dimitris, 2009. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," MPRA Paper 20125, University Library of Munich, Germany.
  5. Lopes, Hedibert Freitas & Moreira, Ajax R. Bello & Schmidt, Alexandra Mello, 1999. "Hyperparameter estimation in forecast models," Computational Statistics & Data Analysis, Elsevier, vol. 29(4), pages 387-410, February.
  6. Matteo Ciccarelli & Alessandro Rebucci, 2003. "Bayesian Vars," IMF Working Papers 03/102, International Monetary Fund.
  7. Lanne, Markku & Lütkepohl, Helmut & Maciejowska, Katarzyna, 2010. "Structural vector autoregressions with Markov switching," Journal of Economic Dynamics and Control, Elsevier, vol. 34(2), pages 121-131, February.
  8. Juan José Echavarría & Enrique López & Sergio Ocampo & Norberto Rodríguez, 2011. "Choques, instituciones laborales y desempleo en Colombia," ENSAYOS SOBRE POLÍTICA ECONÓMICA, BANCO DE LA REPÚBLICA - ESPE.
  9. Ossama Mikhail, 2005. "What Happens After A Technology Shock? A Bayesian Perspective," Macroeconomics 0510016, EconWPA.

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