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Bayesian Vars

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  • Matteo Ciccarelli
  • Alessandro Rebucci

Abstract

This paper reviews recent advances in the specification and estimation of Bayesian Vector Autoregressive models (BVARs). After describing the Bayesian principle of estimation, we first present the methodology originally developed by Litterman (1986) and Doan et al. (1984) and review alternative priors. We then discuss extensions of the basic model and address issues in forecasting and structural analysis. An application to the estimation of a system of time-varying reaction functions for four European central banks under the European Monetary System (EMS) illustrates how some of the results previously presented may be applied in practice.

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Bibliographic Info

Paper provided by International Monetary Fund in its series IMF Working Papers with number 03/102.

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Length: 44
Date of creation: 01 May 2003
Date of revision:
Handle: RePEc:imf:imfwpa:03/102

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Related research

Keywords: Economic models; European Monetary System; forecasting; equation; monetary policy; outliers; covariance; econometrics; time series; probability; statistics; inflation; parameter vector; sampling; equations; error variance; monetary system; structural analysis; random walk; number of variables; numerical integration; monetary aggregate; monte carlo simulation; probability distribution; money supply; linear regression; nonlinear models; monetary reaction functions; central bank; autocorrelation; monetary union; money demand; monetary fund; prediction; diagonal matrix; money market; correlations; time series analysis; estimation procedure; heteroscedasticity; samples; random variable; monetary authorities; normal distribution; causation; sample mean; correlation; survey; monetary growth; mean square; monetary policy instruments; simultaneous equation; probability density; logarithm; bayesian analysis; numerical values; monetary policy instrument; cointegration; integral; linear regression model; european monetary union; probability distributions; functional form; data analysis; probability density function; monetary policy history; numerical integrations; sample size; markov chain; linear models; number of parameters; dynamic system; monte carlo method; monetary policies; normal distributions; monetary policy rules; empirical framework; monetary policy regime; monetary aggregates; estimation method; multivariate distributions; financial statistics; linear trend; optimization; computation; algebra; consistent estimate; coefficient vector; regression analysis; significance level; goodness of fit; probabilities; polynomial; sampling distributions;

References

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  1. Kim, C-J., 1991. "Dynamic Linear Models with Markov-Switching," Papers 91-8, York (Canada) - Department of Economics.
  2. Clarida, Richard & Galí, Jordi & Gertler, Mark, 1997. "Monetary Policy Rules in Practice: Some International Evidence," CEPR Discussion Papers 1750, C.E.P.R. Discussion Papers.
  3. Koop, G, 1992. "Aggregate Shocks and Macroeconomic Fluctuations: A Bayesian Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(4), pages 395-411, Oct.-Dec..
  4. Luis J. Álvarez & Fernando C. Ballabriga, 1994. "BVAR models in the context of cointegration: A Monte Carlo experiment," Banco de Espa�a Working Papers 9405, Banco de Espa�a.
  5. Chib, Siddhartha & Greenberg, Edward, 1995. "Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models," Journal of Econometrics, Elsevier, vol. 68(2), pages 339-360, August.
  6. Zha, Tao, 1999. "Block recursion and structural vector autoregressions," Journal of Econometrics, Elsevier, vol. 90(2), pages 291-316, June.
  7. Ben S. Bernanke & Ilian Mihov, 1996. "What Does the Bundesbank Target?," NBER Working Papers 5764, National Bureau of Economic Research, Inc.
  8. Robert B. Litterman, 1985. "Forecasting with Bayesian vector autoregressions five years of experience," Working Papers 274, Federal Reserve Bank of Minneapolis.
  9. Kadiyala, K Rao & Karlsson, Sune, 1997. "Numerical Methods for Estimation and Inference in Bayesian VAR-Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(2), pages 99-132, March-Apr.
  10. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1983. "Forecasting and Conditional Projection Using Realistic Prior Distributions," NBER Working Papers 1202, National Bureau of Economic Research, Inc.
  11. Christopher A. Sims & Tao Zha, 1996. "Bayesian methods for dynamic multivariate models," Working Paper 96-13, Federal Reserve Bank of Atlanta.
  12. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
  13. Kenen,Peter B., 1995. "Economic and Monetary Union in Europe," Cambridge Books, Cambridge University Press, number 9780521558839, April.
  14. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
  15. Matteo Ciccarelli & Alessandro Rebucci, 2001. "The Transmission Mechanism of European Monetary Policy: Is There Heterogeneity? Is It Changing Over Time?," Banco de Espa�a Working Papers 0115, Banco de Espa�a.
  16. Fabio Canova & Matteo Ciccarelli, 1999. "Forecasting and turning point predictions in a Bayesian panel VAR model," Economics Working Papers 443, Department of Economics and Business, Universitat Pompeu Fabra.
  17. Daniel F. Waggoner & Tao Zha, 1998. "Conditional forecasts in dynamic multivariate models," Working Paper 98-22, Federal Reserve Bank of Atlanta.
  18. Sims, Christopher A, 1972. "Money, Income, and Causality," American Economic Review, American Economic Association, vol. 62(4), pages 540-52, September.
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Cited by:
  1. Caraiani, Petre, 2010. "Forecasting Romanian GDP Using a BVAR Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 76-87, December.

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