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Proxy VAR models in a data-rich environment

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  • Martin Bruns

    (University of East Anglia)

Abstract

Structural VAR models require two ingredients: (i) Informational sufficiency, and (ii) a valid identification strategy. These conditions are unlikely to be met by small-scale recursively identified VAR models. I propose a Bayesian Proxy Factor-Augmented VAR (BP-FAVAR) to combine a large information set with an identification scheme based on an external instrument. In an application to monetary policy shocks I find that augmenting a standard small-scale Proxy VAR by factors from a large set of financial variables changes the model dynamics and delivers price responses which are more in line with economic theory. A second application shows that an exogenous increase in uncertainty affects disaggregated investment series more negatively than consumption series.

Suggested Citation

  • Martin Bruns, 2019. "Proxy VAR models in a data-rich environment," University of East Anglia School of Economics Working Paper Series 2019-03, School of Economics, University of East Anglia, Norwich, UK..
  • Handle: RePEc:uea:ueaeco:2019_03
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    References listed on IDEAS

    as
    1. 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.
    2. Florian Huber & Manfred M. Fischer, 2018. "A Markov Switching Factor‐Augmented VAR Model for Analyzing US Business Cycles and Monetary Policy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(3), pages 575-604, June.
    3. Christiano, Lawrence J. & Eichenbaum, Martin & Evans, Charles L., 1999. "Monetary policy shocks: What have we learned and to what end?," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.),Handbook of Macroeconomics, edition 1, volume 1, chapter 2, pages 65-148, Elsevier.
    4. Ramey, V.A., 2016. "Macroeconomic Shocks and Their Propagation," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.),Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 71-162, Elsevier.
    5. Andrea Carriero & Haroon Mumtaz & Konstantinos Theodoridis & Angeliki Theophilopoulou, 2015. "The Impact of Uncertainty Shocks under Measurement Error: A Proxy SVAR Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(6), pages 1223-1238, September.
    6. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2004. "Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach," Finance and Economics Discussion Series 2004-03, Board of Governors of the Federal Reserve System (U.S.).
    7. Dario Caldara & Edward Herbst, 2019. "Monetary Policy, Real Activity, and Credit Spreads: Evidence from Bayesian Proxy SVARs," American Economic Journal: Macroeconomics, American Economic Association, vol. 11(1), pages 157-192, January.
    8. Mark Kerssenfischer, 2019. "The puzzling effects of monetary policy in VARs: Invalid identification or missing information?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(1), pages 18-25, January.
    9. Thorsten Drautzburg, 2020. "A narrative approach to a fiscal DSGE model," Quantitative Economics, Econometric Society, vol. 11(2), pages 801-837, May.
    10. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575, April.
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    More about this item

    Keywords

    Dynamic factor models; external instruments; monetary policy; uncertainty shocks;

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • E60 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General

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