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Proxy SVAR identification of monetary policy shocks: MonteCarlo evidence and insights for the US

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  • Herwartz, Helmut
  • Rohloff, Hannes
  • Wang, Shu

Abstract

In empirical macroeconomics, proxy structural vector autoregressive models (SVARs) have become a prominent path towards detecting monetary policy (MP) shocks. However, in practice, the merits of proxy SVARs depend on the relevance and exogeneity of the instrumental information employed. Our Monte Carlo analysis sheds light on the performance of proxy SVARs under realistic scenarios of low relative signal strength attached to MP shocks and alternative assumptions on instrument accuracy. In an empirical application with US data we argue in favor of the specific informational content of instruments based on the dynamic stochastic general equilibrium model of Smets andWouters (2007). A joint assessment of the benchmark proxy SVAR and the outcomes of a structural covariance change model imply that from 1973 until 1979 monetary policy contributed on average between 2.2 and 2.4 units of inflation in the GDP deflator. For the so-called Volcker disinflation starting in 1979Q4, the benchmark structural model shows that the Fed's policy measures effectively reduced the GDP deflator within three years (i.e. by -3.06 units until 1982Q3). While the empirical analysis largely conditions ona small-dimensional trinity SVAR, the benchmark proxy SVAR shocks remain remarkably robust within a six-dimensional factor-augmented model comprising rich information from Michael McCracken's database (FRED-QD).

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  • Herwartz, Helmut & Rohloff, Hannes & Wang, Shu, 2020. "Proxy SVAR identification of monetary policy shocks: MonteCarlo evidence and insights for the US," University of Göttingen Working Papers in Economics 404, University of Goettingen, Department of Economics.
  • Handle: RePEc:zbw:cegedp:404
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    1. Alan J. Auerbach & Yuriy Gorodnichenko, 2012. "Measuring the Output Responses to Fiscal Policy," American Economic Journal: Economic Policy, American Economic Association, vol. 4(2), pages 1-27, May.
    2. Christina D. Romer & David H. Romer, 2004. "A New Measure of Monetary Shocks: Derivation and Implications," American Economic Review, American Economic Association, vol. 94(4), pages 1055-1084, September.
    3. Karel Mertens & Morten O. Ravn, 2013. "The Dynamic Effects of Personal and Corporate Income Tax Changes in the United States," American Economic Review, American Economic Association, vol. 103(4), pages 1212-1247, June.
    4. Blanchard, Olivier Jean & Quah, Danny, 1989. "The Dynamic Effects of Aggregate Demand and Supply Disturbances," American Economic Review, American Economic Association, vol. 79(4), pages 655-673, September.
    5. Uhlig, Harald, 2005. "What are the effects of monetary policy on output? Results from an agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 381-419, March.
    6. Silvia Miranda-Agrippino & Giovanni Ricco, 2021. "The Transmission of Monetary Policy Shocks," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(3), pages 74-107, July.
    7. Gouriéroux, Christian & Monfort, Alain & Renne, Jean-Paul, 2017. "Statistical inference for independent component analysis: Application to structural VAR models," Journal of Econometrics, Elsevier, vol. 196(1), pages 111-126.
    8. Markku Lanne & Helmut Lütkepohl, 2008. "Identifying Monetary Policy Shocks via Changes in Volatility," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(6), pages 1131-1149, September.
    9. Efrem Castelnuovo, 2012. "Monetary Policy Neutrality: Sign Restrictions Go to Monte Carlo," "Marco Fanno" Working Papers 0151, Dipartimento di Scienze Economiche "Marco Fanno".
    10. Ravenna, Federico, 2007. "Vector autoregressions and reduced form representations of DSGE models," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2048-2064, October.
    11. Ambrogio Cesa‐Bianchi & Luis Felipe Cespedes & Alessandro Rebucci, 2015. "Global Liquidity, House Prices, and the Macroeconomy: Evidence from Advanced and Emerging Economies," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(S1), pages 301-335, 03.
    12. Lanne, Markku & Saikkonen, Pentti, 2013. "Noncausal Vector Autoregression," Econometric Theory, Cambridge University Press, vol. 29(3), pages 447-481, June.
    13. Lütkepohl, Helmut & Schlaak, Thore, 2018. "Choosing Between Different Time-Varying Volatility Models for Structural Vector Autoregressive Analysis," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue 4, pages 715-735.
    14. Bernanke, Ben S & Blinder, Alan S, 1992. "The Federal Funds Rate and the Channels of Monetary Transmission," American Economic Review, American Economic Association, vol. 82(4), pages 901-921, September.
    15. Silvia Miranda-Agrippino, 2015. "Unsurprising Shocks: Information, Premia, and the Monetary Transmission," Discussion Papers 1613, Centre for Macroeconomics (CFM), revised Apr 2016.
    16. Domenico Giannone & Michele Lenza & Lucrezia Reichlin, 2008. "Explaining The Great Moderation: It Is Not The Shocks," Journal of the European Economic Association, MIT Press, vol. 6(2-3), pages 621-633, 04-05.
    17. 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.
    18. Michael W. McCracken & Serena Ng, 2021. "FRED-QD: A Quarterly Database for Macroeconomic Research," Review, Federal Reserve Bank of St. Louis, vol. 103(1), pages 1-44, January.
    19. Juan Antolín-Díaz & Juan F. Rubio-Ramírez, 2018. "Narrative Sign Restrictions for SVARs," American Economic Review, American Economic Association, vol. 108(10), pages 2802-2829, October.
    20. Christiane Baumeister & Lutz Kilian, 2016. "Forty Years of Oil Price Fluctuations: Why the Price of Oil May Still Surprise Us," Journal of Economic Perspectives, American Economic Association, vol. 30(1), pages 139-160, Winter.
    21. Haroon Mumtaz & Gabor Pinter & Konstantinos Theodoridis, 2018. "What Do Vars Tell Us About The Impact Of A Credit Supply Shock?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(2), pages 625-646, May.
    22. Atif Mian & Amir Sufi & Emil Verner, 2017. "Household Debt and Business Cycles Worldwide," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(4), pages 1755-1817.
    23. Christopher A. Sims & Tao Zha, 2006. "Were There Regime Switches in U.S. Monetary Policy?," American Economic Review, American Economic Association, vol. 96(1), pages 54-81, March.
    24. Ben S. Bernanke & Ilian Mihov, 1998. "Measuring Monetary Policy," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 113(3), pages 869-902.
    25. Marek Jarociński & Peter Karadi, 2020. "Deconstructing Monetary Policy Surprises—The Role of Information Shocks," American Economic Journal: Macroeconomics, American Economic Association, vol. 12(2), pages 1-43, April.
    26. Thore Schlaak & Malte Rieth & Maximilian Podstawski, 2023. "Monetary policy, external instruments, and heteroskedasticity," Quantitative Economics, Econometric Society, vol. 14(1), pages 161-200, January.
    27. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    28. Andrea Giovanni Gazzani & Alejandro Vicondoa, 2019. "Proxy-SVAR as a Bridge for Identification with Higher Frequency Data," 2019 Meeting Papers 855, Society for Economic Dynamics.
    29. Herbst, Edward & Schorfheide, Frank, 2012. "Evaluating DSGE model forecasts of comovements," Journal of Econometrics, Elsevier, vol. 171(2), pages 152-166.
    30. Margaret M. McConnell & Gabriel Perez-Quiros, 2000. "Output fluctuations in the United States: what has changed since the early 1980s?," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
    31. Lanne, Markku & Lütkepohl, Helmut & Maciejowska, Katarzyna, 2010. "Structural vector autoregressions with Markov switching," Journal of Economic Dynamics and Control, Elsevier, vol. 34(2), pages 121-131, February.
    32. 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.
    33. Lanne, Markku & Lütkepohl, Helmut, 2010. "Structural Vector Autoregressions With Nonnormal Residuals," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 159-168.
    34. Mark Gertler & Peter Karadi, 2015. "Monetary Policy Surprises, Credit Costs, and Economic Activity," American Economic Journal: Macroeconomics, American Economic Association, vol. 7(1), pages 44-76, January.
    35. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521547871, September.
    36. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    37. Canova, Fabio & Nicolo, Gianni De, 2002. "Monetary disturbances matter for business fluctuations in the G-7," Journal of Monetary Economics, Elsevier, vol. 49(6), pages 1131-1159, September.
    38. 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, President and Fellows of Harvard College, vol. 120(1), pages 387-422.
    39. Carlstrom, Charles T. & Fuerst, Timothy S. & Paustian, Matthias, 2009. "Monetary policy shocks, Choleski identification, and DNK models," Journal of Monetary Economics, Elsevier, vol. 56(7), pages 1014-1021, October.
    40. 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.
    41. Lanne, Markku & Meitz, Mika & Saikkonen, Pentti, 2017. "Identification and estimation of non-Gaussian structural vector autoregressions," Journal of Econometrics, Elsevier, vol. 196(2), pages 288-304.
    42. Refet S Gürkaynak & Brian Sack & Eric Swanson, 2005. "Do Actions Speak Louder Than Words? The Response of Asset Prices to Monetary Policy Actions and Statements," International Journal of Central Banking, International Journal of Central Banking, vol. 1(1), May.
    43. Herwartz, Helmut & Lange, Alexander & Maxand, Simone, 2019. "Statistical identification in SVARs - Monte Carlo experiments and a comparative assessment of the role of economic uncertainties for the US business cycle," University of Göttingen Working Papers in Economics 375, University of Goettingen, Department of Economics.
    44. Faust, Jon, 1998. "The robustness of identified VAR conclusions about money," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 49(1), pages 207-244, December.
    45. Marek Rusnak & Tomas Havranek & Roman Horvath, 2013. "How to Solve the Price Puzzle? A Meta‐Analysis," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(1), pages 37-70, February.
    46. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    47. Emanuele Bacchiocchi & Luca Fanelli, 2015. "Identification in Structural Vector Autoregressive Models with Structural Changes, with an Application to US Monetary Policy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(6), pages 761-779, December.
    48. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575, September.
    49. Canova, Fabio & Paustian, Matthias, 2011. "Business cycle measurement with some theory," Journal of Monetary Economics, Elsevier, vol. 58(4), pages 345-361.
    50. Paustian Matthias, 2007. "Assessing Sign Restrictions," The B.E. Journal of Macroeconomics, De Gruyter, vol. 7(1), pages 1-33, August.
    51. David S. Matteson & Ruey S. Tsay, 2017. "Independent Component Analysis via Distance Covariance," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 623-637, April.
    52. Christiane Baumeister & James D. Hamilton, 2019. "Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks," American Economic Review, American Economic Association, vol. 109(5), pages 1873-1910, May.
    53. Ambrogio Cesa‐Bianchi & Luis Felipe Cespedes & Alessandro Rebucci, 2015. "Global Liquidity, House Prices, and the Macroeconomy: Evidence from Advanced and Emerging Economies," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(S1), pages 301-335, March.
    54. Robert B. Barsky & Lutz Kilian, 2002. "Do We Really Know That Oil Caused the Great Stagflation? A Monetary Alternative," NBER Chapters, in: NBER Macroeconomics Annual 2001, Volume 16, pages 137-198, National Bureau of Economic Research, Inc.
    55. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521839198, September.
    56. Coibion, Olivier & Gorodnichenko, Yuriy & Kueng, Lorenz & Silvia, John, 2017. "Innocent Bystanders? Monetary policy and inequality," Journal of Monetary Economics, Elsevier, vol. 88(C), pages 70-89.
    57. Carsten Jentsch & Kurt G. Lunsford, 2019. "The Dynamic Effects of Personal and Corporate Income Tax Changes in the United States: Comment," American Economic Review, American Economic Association, vol. 109(7), pages 2655-2678, July.
    58. Roberto Rigobon, 2003. "Identification Through Heteroskedasticity," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 777-792, November.
    59. Lutz Kilian & Daniel P. Murphy, 2014. "The Role Of Inventories And Speculative Trading In The Global Market For Crude Oil," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 454-478, April.
    60. Hamidi Sahneh, Mehdi, 2016. "Testing for Non-Fundamentalness," MPRA Paper 71924, University Library of Munich, Germany.
    61. Barakchian, S. Mahdi & Crowe, Christopher, 2013. "Monetary policy matters: Evidence from new shocks data," Journal of Monetary Economics, Elsevier, vol. 60(8), pages 950-966.
    62. Helmut Herwartz & Martin Plödt, 2016. "Simulation Evidence on Theory-based and Statistical Identification under Volatility Breaks," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(1), pages 94-112, February.
    63. Christian K. Wolf, 2020. "SVAR (Mis)identification and the Real Effects of Monetary Policy Shocks," American Economic Journal: Macroeconomics, American Economic Association, vol. 12(4), pages 1-32, October.
    64. James H. Stock & Mark W. Watson, 2012. "Disentangling the Channels of the 2007-2009 Recession," NBER Working Papers 18094, National Bureau of Economic Research, Inc.
    65. Renee Fry & Adrian Pagan, 2007. "Some Issues in Using Sign Restrictions for Identifying Structural VARs," NCER Working Paper Series 14, National Centre for Econometric Research.
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    Keywords

    structural vector autoregression; external instruments; proxy SVAR; heteroskedasticity; monetary policy shocks;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

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