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Applied Bayesian econometrics for central bankers


  • Andrew P Blake
  • Haroon Mumtaz


The aim of this handbook is to introduce key topics in Bayesian econometrics from an applied perspective. The handbook assumes that readers have a fair grasp of basic classical econometrics (e.g. maximum likelihood estimation). It is recommended that readers familiarise themselves with Matlab© programming language to derive the maximum benefit from this handbook. A basic guide to Matlab© is attached at the end of the handbook. The first chapter of the handbook introduces basic concepts of Bayesian analysis. In particular, the chapter focuses on the technique of Gibbs sampling and applies it to a linear regression model. The chapter shows how to code this algorithm via several practical examples. The second chapter introduces Bayesian vector autoregressions (VARs) and discusses how Gibbs sampling can be used for these models. The third chapter shows how Gibbs sampling can be applied to popular econometric models such as time-varying VARs and dynamic factor models. The final chapter introduces the Metropolis Hastings algorithm. We intend to introduce new topics in revised versions of this handbook on a regular basis. The handbook comes with a set of Matlab© codes that can be used to replicate the examples in each chapter. The code (provided in is organised by chapter. For example, the folder 'Chapter 1' contains all the examples referred to in the first chapter of this handbook. The views expressed in this handbook are those of the authors, and not necessarily those of the Bank of England. The reference material and computer codes are provided without any guarantee of accuracy.

Suggested Citation

  • Andrew P Blake & Haroon Mumtaz, 2012. "Applied Bayesian econometrics for central bankers," Technical Books, Centre for Central Banking Studies, Bank of England, edition 1, number 4.
  • Handle: RePEc:ccb:tbooks:4

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    References listed on IDEAS

    1. Robertson, John C & Tallman, Ellis W & Whiteman, Charles H, 2005. "Forecasting Using Relative Entropy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 383-401, June.
    2. Juan F. Rubio-Ramírez & Daniel F. Waggoner & Tao Zha, 2010. "Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference," Review of Economic Studies, Oxford University Press, vol. 77(2), pages 665-696.
    3. Daniel F. Waggoner & Tao Zha, 1999. "Conditional Forecasts In Dynamic Multivariate Models," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 639-651, November.
    4. John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, issue Q1, pages 4-18.
    5. Sims, Christopher A & Zha, Tao, 1998. "Bayesian Methods for Dynamic Multivariate Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-968, November.
    6. Mattias Villani, 2009. "Steady-state priors for vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 630-650.
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    Cited by:

    1. Byrne, Joseph P. & Korobilis, Dimitris & Ribeiro, Pinho J., 2016. "Exchange rate predictability in a changing world," Journal of International Money and Finance, Elsevier, vol. 62(C), pages 1-24.
    2. Deryugina, Elena & Ponomarenko, Alexey & Sinyakov, Andrey & Sorokin, Constantine, 2015. "Evaluating underlying inflation measures for Russia," BOFIT Discussion Papers 24/2015, Bank of Finland, Institute for Economies in Transition.
    3. William Gatt, 2016. "Time variation, asymmetry and threshold effect in Malta's Phillips curve," CBM Working Papers WP/02/2016, Central Bank of Malta.
    4. Mendonça, Diogo de Prince & Marçal, Emerson Fernandes & Brito, Márcio Holland de, 2016. "Is fiscal policy effective in Brazil? An empirical analysis," Textos para discussão 433, FGV/EESP - Escola de Economia de São Paulo, Getulio Vargas Foundation (Brazil).
    5. Corina SAMAN, 2016. "The Impact of the US and Euro Area Financial Systemic Stress to the Romanian Economy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 170-183, December.
    6. Malgorzata Skibinska, 2017. "Transmission of monetary policy and exchange rate shocks under foreign currency lending," Working Papers 2017-027, Warsaw School of Economics, Collegium of Economic Analysis.
    7. repec:spr:empeco:v:53:y:2017:i:2:d:10.1007_s00181-016-1125-1 is not listed on IDEAS
    8. Mihaela Simionescu (Bratu), 2014. "The Bayesian Modelling Of Inflation Rate In Romania," Romanian Statistical Review, Romanian Statistical Review, vol. 62(2), pages 147-160, June.
    9. Nelson, Benjamin & Pinter, Gabor & Theodoridis, Konstantinos, 2015. "Do contractionary monetary policy shocks expand shadow banking?," Bank of England working papers 521, Bank of England.
    10. Pestova, Anna & Mamonov, Mikhail, 2016. "Estimating the Influence of Different Shocks on Macroeconomic Indicators and Developing Conditional Forecasts on the Basis of BVAR Model for the Russian Economy," Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 4, pages 56-92, August.
    11. repec:eee:intfor:v:33:y:2017:i:3:p:591-604 is not listed on IDEAS
    12. Yasemin Erduman & Neslihan Kaya, 2014. "Determinants of Bond Flows to Emerging Markets: How Do They Change Over Time?," Working Papers 1428, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    13. Owen Grech & Noel Rapa, 2016. "STREAM: A structural macro-econometric model of the Maltese economy," CBM Working Papers WP/01/2016, Central Bank of Malta.
    14. Lomivorotov, Rodion, 2015. "Bayesian estimation of monetary policy in Russia," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 38(2), pages 41-63.
    15. Matei KUBINSCHI & Dinu BARNEA, 2016. "Systemic Risk Impact on Economic Growth - The Case of the CEE Countries," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 79-94, December.
    16. Dennis Bonam & Jakob de Haan & Duncan van Limbergen, 2018. "Time-varying wage Phillips curves in the euro area with a new measure for labor market slack," DNB Working Papers 587, Netherlands Central Bank, Research Department.

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    Applied Bayesian; econometrics;


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