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Time-Varying Parameter VAR Model with Stochastic Volatility: An Overview of Methodology and Empirical Applications

  • Jouchi Nakajima

    (Institute for Monetary and Economic Studies, Bank of Japan (Currently in the Personnel and Corporate Affairs Department )

This paper aims to provide a comprehensive overview of the estimation methodology for the time-varying parameter structural vector autoregression (TVP-VAR) with stochastic volatility, in both methodology and empirical applications. The TVP-VAR model, combined with stochastic volatility, enables us to capture possible changes in underlying structure of the economy in a flexible and robust manner. In that respect, as shown in simulation exercises in the paper, the incorporation of stochastic volatility to the TVP estimation significantly improves estimation performance. The Markov chain Monte Carlo (MCMC) method is employed for the estimation of the TVP-VAR models with stochastic volatility. As an example of empirical application, the TVP-VAR model with stochastic volatility is estimated using the Japanese data with significant structural changes in dynamic relationship between the macroeconomic variables.

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File URL: http://www.imes.boj.or.jp/research/papers/english/11-E-09.pdf
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Paper provided by Institute for Monetary and Economic Studies, Bank of Japan in its series IMES Discussion Paper Series with number 11-E-09.

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Date of creation: Mar 2011
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Handle: RePEc:ime:imedps:11-e-09
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  1. Dimitris Korompilis, 2009. "Assessing the Transmission of Monetary Policy Shocks Using Dynamic Factor Models," Working Papers 0914, University of Strathclyde Business School, Department of Economics.
  2. D'Agostino, Antonello & Gambetti, Luca & Giannone, Domenico, 2009. "Macroeconomic Forecasting and Structural Change," CEPR Discussion Papers 7542, C.E.P.R. Discussion Papers.
  3. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  4. Kim, Sangjoon & Shephard, Neil & Chib, Siddhartha, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Wiley Blackwell, vol. 65(3), pages 361-93, July.
  5. Toshitaka Sekine, 2006. "Time-varying exchange rate pass-through: experiences of some industrial countries," BIS Working Papers 202, Bank for International Settlements.
  6. Baumeister, Christiane & Benati, Luca, 2010. "Unconventional monetary policy and the great recession - Estimating the impact of a compression in the yield spread at the zero lower bound," Working Paper Series 1258, European Central Bank.
  7. Mumtaz, Haroon & Surico, Paolo, 2008. "Time-Varying Yield Curve Dynamics and Monetary Policy," Discussion Papers 23, Monetary Policy Committee Unit, Bank of England.
  8. Benati, Luca, 2007. "The ‘Great Moderation’ in the United Kingdom," Working Paper Series 0769, European Central Bank.
  9. Eric M. Leeper & Christopher A. Sims & Tao Zha, 1996. "What Does Monetary Policy Do?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 27(2), pages 1-78.
  10. Benati, Luca & Surico, Paolo, 2007. "Evolving U.S. monetary policy and the decline of inflation predictability," Working Paper Series 0824, European Central Bank.
  11. Jouchi Nakajima & Yuki Teranishi, 2009. "The Evolution of Loan Rate Stickiness Across the Euro Area," IMES Discussion Paper Series 09-E-10, Institute for Monetary and Economic Studies, Bank of Japan.
  12. Nakajima, Jouchi & Kasuya, Munehisa & Watanabe, Toshiaki, 2011. "Bayesian analysis of time-varying parameter vector autoregressive model for the Japanese economy and monetary policy," Journal of the Japanese and International Economies, Elsevier, vol. 25(3), pages 225-245, September.
  13. Toshitaka Sekine & Yuki Teranishi, 2008. "Inflation Targeting and Monetary Policy Activism," IMES Discussion Paper Series 08-E-13, Institute for Monetary and Economic Studies, Bank of Japan.
  14. Nakajima Jouchi, 2011. "Monetary Policy Transmission under Zero Interest Rates: An Extended Time-Varying Parameter Vector Autoregression Approach," The B.E. Journal of Macroeconomics, De Gruyter, vol. 11(1), pages 1-24, October.
  15. Chib, Siddhartha & Greenberg, Edward, 1996. "Markov Chain Monte Carlo Simulation Methods in Econometrics," Econometric Theory, Cambridge University Press, vol. 12(03), pages 409-431, August.
  16. Omori, Yasuhiro & Chib, Siddhartha & Shephard, Neil & Nakajima, Jouchi, 2007. "Stochastic volatility with leverage: Fast and efficient likelihood inference," Journal of Econometrics, Elsevier, vol. 140(2), pages 425-449, October.
  17. Jouchi Nakajima & Shigenori Shiratsuka & Yuki Teranishi, 2010. "The Effects of Monetary Policy Commitment: Evidence from Time- varying Parameter VAR Analysis," IMES Discussion Paper Series 10-E-06, Institute for Monetary and Economic Studies, Bank of Japan.
  18. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
  19. Harald Uhlig, 1997. "Bayesian Vector Autoregressions with Stochastic Volatility," Econometrica, Econometric Society, vol. 65(1), pages 59-74, January.
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