IDEAS home Printed from https://ideas.repec.org/p/ime/imedps/11-e-09.html
   My bibliography  Save this paper

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

Author

Listed:
  • Jouchi Nakajima

    (Institute for Monetary and Economic Studies, Bank of Japan (Currently in the Personnel and Corporate Affairs Department , E-mail: jouchi.nakajima@stat.duke.edu))

Abstract

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.

Suggested Citation

  • Jouchi Nakajima, 2011. "Time-Varying Parameter VAR Model with Stochastic Volatility: An Overview of Methodology and Empirical Applications," IMES Discussion Paper Series 11-E-09, Institute for Monetary and Economic Studies, Bank of Japan.
  • Handle: RePEc:ime:imedps:11-e-09
    as

    Download full text from publisher

    File URL: http://www.imes.boj.or.jp/research/papers/english/11-E-09.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Luca Benati, 2008. "The "Great Moderation" in the United Kingdom," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(1), pages 121-147, February.
    2. Haroon Mumtaz & Paolo Surico, 2009. "Time-varying yield curve dynamics and monetary policy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(6), pages 895-913.
    3. Chib, Siddhartha & Greenberg, Edward, 1996. "Markov Chain Monte Carlo Simulation Methods in Econometrics," Econometric Theory, Cambridge University Press, vol. 12(3), pages 409-431, August.
    4. Luca Benati & Paolo Surico, 2008. "Evolving U.S. Monetary Policy and The Decline of Inflation Predictability," Journal of the European Economic Association, MIT Press, vol. 6(2-3), pages 634-646, 04-05.
    5. Dimitris Korobilis, 2013. "Assessing the Transmission of Monetary Policy Using Time-varying Parameter Dynamic Factor Models-super-," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(2), pages 157-179, April.
    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. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    8. 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.
    9. 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.
    10. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    11. Toshitaka Sekine, 2006. "Time-varying exchange rate pass-through: experiences of some industrial countries," BIS Working Papers 202, Bank for International Settlements.
    12. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 361-393.
    13. Antonello D'Agostino & Luca Gambetti & Domenico Giannone, 2013. "Macroeconomic forecasting and structural change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(1), pages 82-101, January.
    14. 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.
    15. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, Oxford University Press, vol. 120(1), pages 387-422.
    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. Harald Uhlig, 1997. "Bayesian Vector Autoregressions with Stochastic Volatility," Econometrica, Econometric Society, vol. 65(1), pages 59-74, January.
    19. 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.
    20. 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.
    21. 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.
    22. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," Review of Economic Studies, Oxford University Press, vol. 72(3), pages 821-852.
    23. Christiane Baumeister & Eveline Durinck & Gert Peersman, 2008. "Liquidity, inflation and asset prices in a time-varying framework for the euro area," Working Paper Research 142, National Bank of Belgium.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kang, Sang Hoon & Islam, Faridul & Kumar Tiwari, Aviral, 2019. "The dynamic relationships among CO2 emissions, renewable and non-renewable energy sources, and economic growth in India: Evidence from time-varying Bayesian VAR model," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 90-101.
    2. Punzi, Maria Teresa, 2016. "Financial cycles and co-movements between the real economy, finance and asset price dynamics in large-scale crises," FinMaP-Working Papers 61, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    3. 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.
    4. Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 791-897, Elsevier.
    5. Chance Ngamanya Mwabutwa & Nicola Viegi & Manoel Bittencourt, 2016. "Evolution Of Monetary Policy Transmission Mechanism In Malawi: A Tvp-Var Approach," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 41(1), pages 33-55, March.
    6. He, Yongda & Lin, Boqiang, 2018. "Time-varying effects of cyclical fluctuations in China's energy industry on the macro economy and carbon emissions," Energy, Elsevier, vol. 155(C), pages 1102-1112.
    7. 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.
    8. Arratibel, Olga & Michaelis, Henrike, 2013. "The Impact of Monetary Policy and Exchange Rate Shocks in Poland: Evidence from a Time-Varying VAR," Discussion Papers in Economics 21088, University of Munich, Department of Economics.
    9. Han, Jong-Suk & Hur, Joonyoung, 2020. "Macroeconomic effects of monetary policy in Korea: A time-varying coefficient VAR approach," Economic Modelling, Elsevier, vol. 89(C), pages 142-152.
    10. Mohanty, Deepak & John, Joice, 2015. "Determinants of inflation in India," Journal of Asian Economics, Elsevier, vol. 36(C), pages 86-96.
    11. Gil-Alana, Luis A. & Gupta, Rangan & Olubusoye, Olusanya E. & Yaya, OlaOluwa S., 2016. "Time series analysis of persistence in crude oil price volatility across bull and bear regimes," Energy, Elsevier, vol. 109(C), pages 29-37.
    12. Legrand, Romain, 2018. "Time-Varying Vector Autoregressions: Efficient Estimation, Random Inertia and Random Mean," MPRA Paper 88925, University Library of Munich, Germany.
    13. Moussa, Zakaria, 2010. "The Japanese Quantitative Easing Policy under Scrutiny: A Time-Varying Parameter Factor-Augmented VAR Model," MPRA Paper 29429, University Library of Munich, Germany.
    14. Nalan Basturk & Cem Cakmakli & S. Pinar Ceyhan & Herman K. van Dijk, 2014. "On the Rise of Bayesian Econometrics after Cowles Foundation Monographs 10, 14," Tinbergen Institute Discussion Papers 14-085/III, Tinbergen Institute, revised 04 Sep 2014.
    15. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2020. "The economic drivers of volatility and uncertainty," Temi di discussione (Economic working papers) 1285, Bank of Italy, Economic Research and International Relations Area.
    16. Jouchi Nakajima & Mike West, 2013. "Bayesian Analysis of Latent Threshold Dynamic Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 151-164, April.
    17. Joshua C.C. Chan & Rodney W. Strachan, 2020. "Bayesian state space models in macroeconometrics," CAMA Working Papers 2020-90, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    18. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87, May.
    19. Mandalinci, Zeyyad, 2017. "Forecasting inflation in emerging markets: An evaluation of alternative models," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1082-1104.
    20. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2018. "The global component of inflation volatility," Temi di discussione (Economic working papers) 1170, Bank of Italy, Economic Research and International Relations Area.

    More about this item

    Keywords

    Bayesian inference; Markov chain Monte Carlo; Monetary policy; State space model; Structural vector autoregression; Stochastic volatility; Time-varying parameter;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ime:imedps:11-e-09. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/imegvjp.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kinken (email available below). General contact details of provider: https://edirc.repec.org/data/imegvjp.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.