IDEAS home Printed from https://ideas.repec.org/p/boe/boeewp/0961.html
   My bibliography  Save this paper

Identification of SVAR models by combining sign restrictions with external instruments

Author

Listed:
  • Braun, Robin

    (Bank of England)

  • Brüggemann, Ralf

    (University of Konstanz)

Abstract

We discuss combining sign restrictions with information in external instruments (proxy variables) to identify structural vector autoregressive (SVAR) models. In one setting, we assume the availability of valid external instruments. Sign restrictions may then be used to identify further orthogonal shocks, or as an additional piece of information to pin down the shocks identified by the external instruments more precisely. In a second setting, we assume that proxy variables are only ‘plausibly exogenous’ and suggest various types of inequality restrictions to bound the relation between structural shocks and the external variable. This can be combined with conventional sign restrictions to further narrow down the set of admissible models. Within a proxy-augmented SVAR, we conduct Bayesian inference and discuss computation of Bayes factors. They can be useful to test either the sign or IV restrictions as overidentifying. We illustrate the usefulness of our methodology in estimating the effects of oil supply and monetary policy shocks.

Suggested Citation

  • Braun, Robin & Brüggemann, Ralf, 2022. "Identification of SVAR models by combining sign restrictions with external instruments," Bank of England working papers 961, Bank of England.
    • Handle: RePEc:boe:boeewp:0961
    as

    Download full text from publisher

    File URL: https://www.bankofengland.co.uk/-/media/boe/files/working-paper/2022/identification-of-svar-models-by-combining-sign-restrictions-with-external-instruments.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. Karel Mertens & Morten O. Ravn, 2012. "Empirical Evidence on the Aggregate Effects of Anticipated and Unanticipated US Tax Policy Shocks," American Economic Journal: Economic Policy, American Economic Association, vol. 4(2), pages 145-181, May.
    5. Martin Bruns & Michele Piffer, 2021. "Monetary policy shocks over the business cycle: Extending the Smooth Transition framework," University of East Anglia School of Economics Working Paper Series 2021-07, School of Economics, University of East Anglia, Norwich, UK..
    6. Baumeister, Christiane & Hamilton, James D., 2018. "Inference in structural vector autoregressions when the identifying assumptions are not fully believed: Re-evaluating the role of monetary policy in economic fluctuations," Journal of Monetary Economics, Elsevier, vol. 100(C), pages 48-65.
    7. 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.
    8. Jamie L. Cross & Bao H. Nguyen & Trung Duc Tran, 2022. "The role of precautionary and speculative demand in the global market for crude oil," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 882-895, August.
    9. Karel Mertens & José Luis Montiel Olea, 2018. "Marginal Tax Rates and Income: New Time Series Evidence," The Quarterly Journal of Economics, Oxford University Press, vol. 133(4), pages 1803-1884.
    10. Jonas E. Arias & Juan Rubio-Ramirez & Daniel F. Waggoner, 2013. "Inference Based on SVARs Identied with Sign and Zero Restrictions: Theory and Applications," Working Papers 2013-24, FEDEA.
    11. Renée Fry & Adrian Pagan, 2011. "Sign Restrictions in Structural Vector Autoregressions: A Critical Review," Journal of Economic Literature, American Economic Association, vol. 49(4), pages 938-960, December.
    12. Boer, Lukas & Lütkepohl, Helmut, 2021. "Qualitative versus quantitative external information for proxy vector autoregressive analysis," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    13. Arias, Jonas E. & Caldara, Dario & Rubio-Ramírez, Juan F., 2019. "The systematic component of monetary policy in SVARs: An agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 101(C), pages 1-13.
    14. Juan F. Rubio-Ramírez & Daniel F. Waggoner & Tao Zha, 2010. "Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(2), pages 665-696.
    15. Ana María Herrera & Sandeep Kumar Rangaraju, 2020. "The effect of oil supply shocks on US economic activity: What have we learned?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(2), pages 141-159, March.
    16. 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.
    17. Montiel Olea, José L. & Stock, James H. & Watson, Mark W., 2021. "Inference in Structural Vector Autoregressions identified with an external instrument," Journal of Econometrics, Elsevier, vol. 225(1), pages 74-87.
    18. Thorsten Drautzburg, 2020. "A narrative approach to a fiscal DSGE model," Quantitative Economics, Econometric Society, vol. 11(2), pages 801-837, May.
    19. Hamilton, James D., 2003. "What is an oil shock?," Journal of Econometrics, Elsevier, vol. 113(2), pages 363-398, April.
    20. Christiane Baumeister & Gert Peersman, 2013. "The Role Of Time‐Varying Price Elasticities In Accounting For Volatility Changes In The Crude Oil Market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(7), pages 1087-1109, November.
    21. Sydney C. Ludvigson & Sai Ma & Serena Ng, 2017. "Shock Restricted Structural Vector-Autoregressions," NBER Working Papers 23225, National Bureau of Economic Research, Inc.
    22. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575.
    23. Faust, Jon & Swanson, Eric T. & Wright, Jonathan H., 2004. "Identifying VARS based on high frequency futures data," Journal of Monetary Economics, Elsevier, vol. 51(6), pages 1107-1131, September.
    24. Olivier Coibion, 2012. "Are the Effects of Monetary Policy Shocks Big or Small?," American Economic Journal: Macroeconomics, American Economic Association, vol. 4(2), pages 1-32, April.
    25. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
    26. 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.
    27. Peersman, Gert & Van Robays, Ine, 2012. "Cross-country differences in the effects of oil shocks," Energy Economics, Elsevier, vol. 34(5), pages 1532-1547.
    28. Baumeister, Christiane & Hamilton, James, 2018. "Inference in Structural Vector Autoregressions When the Identifying Assumptions are Not Fully Believed: Re-evaluating the Role," CEPR Discussion Papers 12911, C.E.P.R. Discussion Papers.
    29. Christiane Baumeister & James D. Hamilton, 2015. "Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information," Econometrica, Econometric Society, vol. 83(5), pages 1963-1999, September.
    30. Simon Gilchrist & Egon Zakrajsek, 2012. "Credit Spreads and Business Cycle Fluctuations," American Economic Review, American Economic Association, vol. 102(4), pages 1692-1720, June.
    31. 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.
    32. 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.
    33. 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.
    34. Mikkel Plagborg-Møller & Christian K. Wolf, 2022. "Instrumental Variable Identification of Dynamic Variance Decompositions," Journal of Political Economy, University of Chicago Press, vol. 130(8), pages 2164-2202.
    35. J. B. Taylor & Harald Uhlig (ed.), 2016. "Handbook of Macroeconomics," Handbook of Macroeconomics, Elsevier, edition 1, volume 2, number 2.
    36. Giovanni Angelini & Luca Fanelli, 2019. "Exogenous uncertainty and the identification of structural vector autoregressions with external instruments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 951-971, September.
    37. Dominik Bertsche, 2019. "The effects of oil supply shocks on the macroeconomy: a Proxy-FAVAR approachThe effects of oil supply shocks on the macroeconomy: a Proxy-FAVAR approach," Working Paper Series of the Department of Economics, University of Konstanz 2019-06, Department of Economics, University of Konstanz.
    38. Caldara, Dario & Cavallo, Michele & Iacoviello, Matteo, 2019. "Oil price elasticities and oil price fluctuations," Journal of Monetary Economics, Elsevier, vol. 103(C), pages 1-20.
    39. Helmut Lütkepohl & Aleksei Netšunajev, 2015. "Structural Vector Autoregressions with Heteroskedasticy," SFB 649 Discussion Papers SFB649DP2015-015, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    40. James H. Stock & Mark W. Watson, 2012. "Disentangling the Channels of the 2007-09 Recession," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 43(1 (Spring), pages 81-156.
    41. Lutz Kilian, 2008. "Exogenous Oil Supply Shocks: How Big Are They and How Much Do They Matter for the U.S. Economy?," The Review of Economics and Statistics, MIT Press, vol. 90(2), pages 216-240, May.
    42. Christina D. Romer & David H. Romer, 1989. "Does Monetary Policy Matter? A New Test in the Spirit of Friedman and Schwartz," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 121-184, National Bureau of Economic Research, Inc.
    43. Uhrin, Gábor B. & Herwartz, Helmut, 2016. "Monetary policy shocks, set-identifying restrictions, and asset prices: A benchmarking approach for analyzing set-identified models," University of Göttingen Working Papers in Economics 295, University of Goettingen, Department of Economics.
    44. Lutz Kilian, 2008. "The Economic Effects of Energy Price Shocks," Journal of Economic Literature, American Economic Association, vol. 46(4), pages 871-909, December.
    45. Emi Nakamura & Jón Steinsson, 2018. "High-Frequency Identification of Monetary Non-Neutrality: The Information Effect," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(3), pages 1283-1330.
    46. Mertens, Karel & Ravn, Morten O., 2014. "A reconciliation of SVAR and narrative estimates of tax multipliers," Journal of Monetary Economics, Elsevier, vol. 68(S), pages 1-19.
    47. repec:zbw:bofrdp:2018_014 is not listed on IDEAS
    48. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    49. Lütkepohl, Helmut & Netšunajev, Aleksei, 2017. "Structural vector autoregressions with heteroskedasticity: A review of different volatility models," Econometrics and Statistics, Elsevier, vol. 1(C), pages 2-18.
    50. Cesa-Bianchi, Ambrogio & Sokol, Andrej, 2022. "Financial shocks, credit spreads, and the international credit channel," Journal of International Economics, Elsevier, vol. 135(C).
    51. Lutz Kilian & Daniel P. Murphy, 2012. "Why Agnostic Sign Restrictions Are Not Enough: Understanding The Dynamics Of Oil Market Var Models," Journal of the European Economic Association, European Economic Association, vol. 10(5), pages 1166-1188, October.
    52. Rubio-Ramírez, Juan Francisco & Caldara, Dario & Arias, Jonas E., 2016. "The Systematic Component of Monetary Policy in SVARs: An Agnostic Identi," CEPR Discussion Papers 11674, C.E.P.R. Discussion Papers.
    53. 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.
    54. 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.
    55. 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.
    56. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    57. 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.
    58. Jonas E. Arias & Juan F. Rubio‐Ramírez & Daniel F. Waggoner, 2018. "Inference Based on Structural Vector Autoregressions Identified With Sign and Zero Restrictions: Theory and Applications," Econometrica, Econometric Society, vol. 86(2), pages 685-720, March.
    59. 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.
    60. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
    61. Markku Lanne & Jani Luoto, 2020. "Identification of Economic Shocks by Inequality Constraints in Bayesian Structural Vector Autoregression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(2), pages 425-452, April.
    62. 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.
    63. Martin Bruns & Michele Piffer, 2018. "Bayesian Structural VAR models: a new approach for prior beliefs on impulse responses," Working Papers 878, Queen Mary University of London, School of Economics and Finance.
    64. James H. Stock & Mark W. Watson, 2012. "Disentangling the Channels of the 2007-09 Recession," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 44(1 (Spring), pages 81-156.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Giacomini, Raffaella & Kitagawa, Toru & Read, Matthew, 2022. "Robust Bayesian inference in proxy SVARs," Journal of Econometrics, Elsevier, vol. 228(1), pages 107-126.
    2. Jörg Breitung & Ralf Brüggemann, 2019. "Projection estimators for structural impulse responses," Working Paper Series of the Department of Economics, University of Konstanz 2019-05, Department of Economics, University of Konstanz.
    3. Dominik Bertsche & Robin Braun, 2022. "Identification of Structural Vector Autoregressions by Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 328-341, January.
    4. Masud Alam, 2021. "Heterogeneous Responses to the U.S. Narrative Tax Changes: Evidence from the U.S. States," Papers 2107.13678, arXiv.org.
    5. Arias, Jonas E. & Rubio-Ramírez, Juan F. & Waggoner, Daniel F., 2021. "Inference in Bayesian Proxy-SVARs," Journal of Econometrics, Elsevier, vol. 225(1), pages 88-106.
    6. Dominik Bertsche, 2019. "The effects of oil supply shocks on the macroeconomy: a Proxy-FAVAR approachThe effects of oil supply shocks on the macroeconomy: a Proxy-FAVAR approach," Working Paper Series of the Department of Economics, University of Konstanz 2019-06, Department of Economics, University of Konstanz.
    7. Habib, Maurizio Michael & Venditti, Fabrizio, 2019. "The global capital flows cycle: structural drivers and transmission channels," Working Paper Series 2280, European Central Bank.
    8. Venditti, Fabrizio & Veronese, Giovanni, 2020. "Global financial markets and oil price shocks in real time," Working Paper Series 2472, European Central Bank.
    9. Arbatli-Saxegaard, Elif & Furceri, Davide & Gonzalez Dominguez, Pablo & Ostry, Jonathan & Peiris, Shanaka, 2022. "Spillovers from US Monetary Shocks: Role of Policy Drivers and Cyclical Conditions," ADBI Working Papers 1317, Asian Development Bank Institute.
    10. Marco Bernardini & Antonio M. Conti, 2023. "Announcement and implementation effects of central bank asset purchases," Temi di discussione (Economic working papers) 1435, Bank of Italy, Economic Research and International Relations Area.
    11. Sascha A. Keweloh & Mathias Klein & Jan Pruser, 2023. "Estimating Fiscal Multipliers by Combining Statistical Identification with Potentially Endogenous Proxies," Papers 2302.13066, arXiv.org, revised Feb 2024.
    12. Habib, Maurizio Michael & Stracca, Livio & Venditti, Fabrizio, 2020. "The fundamentals of safe assets," Journal of International Money and Finance, Elsevier, vol. 102(C).
    13. Francesco Fusari, 2023. "Identifying Monetary Policy Shocks Through External Variable Constraints," School of Economics Discussion Papers 0123, School of Economics, University of Surrey.
    14. Karel Mertens & Morten O. Ravn, 2018. "The Dynamic Effects of Personal and Corporate Income Tax Changes in the United States: Reply to Jentsch and Lunsford," Working Papers 1805, Federal Reserve Bank of Dallas.
    15. Herwartz, Helmut & Wang, Shu, 2023. "Point estimation in sign-restricted SVARs based on independence criteria with an application to rational bubbles," Journal of Economic Dynamics and Control, Elsevier, vol. 151(C).

    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. Robin Braun & Ralf Brüggemann, 2020. "Identification of SVAR Models by Combining Sign Restrictions With External Instruments," Working Paper Series of the Department of Economics, University of Konstanz 2020-01, Department of Economics, University of Konstanz.
    2. 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.
    3. Dominik Bertsche, 2019. "The effects of oil supply shocks on the macroeconomy: a Proxy-FAVAR approachThe effects of oil supply shocks on the macroeconomy: a Proxy-FAVAR approach," Working Paper Series of the Department of Economics, University of Konstanz 2019-06, Department of Economics, University of Konstanz.
    4. Dominik Bertsche & Robin Braun, 2022. "Identification of Structural Vector Autoregressions by Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 328-341, January.
    5. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    6. Giacomini, Raffaella & Kitagawa, Toru & Read, Matthew, 2022. "Robust Bayesian inference in proxy SVARs," Journal of Econometrics, Elsevier, vol. 228(1), pages 107-126.
    7. Kilian, Lutz, 2022. "Facts and fiction in oil market modeling," Energy Economics, Elsevier, vol. 110(C).
    8. Mikkel Plagborg‐Møller & Christian K. Wolf, 2021. "Local Projections and VARs Estimate the Same Impulse Responses," Econometrica, Econometric Society, vol. 89(2), pages 955-980, March.
    9. Herwartz, Helmut & Rohloff, Hannes & Wang, Shu, 2022. "Proxy SVAR identification of monetary policy shocks - Monte Carlo evidence and insights for the US," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    10. Rüth, Sebastian K., 2020. "Shifts in monetary policy and exchange rate dynamics: Is Dornbusch's overshooting hypothesis intact, after all?," Journal of International Economics, Elsevier, vol. 126(C).
    11. Laumer, Sebastian, 2020. "Government spending and heterogeneous consumption dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 114(C).
    12. Ferreira, Leonardo N., 2022. "Forward guidance matters: Disentangling monetary policy shocks," Journal of Macroeconomics, Elsevier, vol. 73(C).
    13. Baumeister, Christiane & Hamilton, James D., 2020. "Drawing conclusions from structural vector autoregressions identified on the basis of sign restrictions," Journal of International Money and Finance, Elsevier, vol. 109(C).
    14. Rüth, Sebastian K., 2019. "Shifts in Monetary Policy and Exchange Rate Dynamics: Is Dornbusch's Overshooting Hypothesis Intact, After all?," Working Papers 0673, University of Heidelberg, Department of Economics.
    15. 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.
    16. Pascal Paul, 2020. "The Time-Varying Effect of Monetary Policy on Asset Prices," The Review of Economics and Statistics, MIT Press, vol. 102(4), pages 690-704, October.
    17. Arias, Jonas E. & Caldara, Dario & Rubio-Ramírez, Juan F., 2019. "The systematic component of monetary policy in SVARs: An agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 101(C), pages 1-13.
    18. Lukas Boer & Andrea Pescatori & Martin Stuermer, 2021. "Energy Transition Metals," Discussion Papers of DIW Berlin 1976, DIW Berlin, German Institute for Economic Research.
    19. Nadav Ben Zeev, 2019. "Is There A Single Shock That Drives The Majority Of Business Cycle Fluctuations?," Working Papers 1906, Ben-Gurion University of the Negev, Department of Economics.
    20. Uhrin, Gábor B. & Herwartz, Helmut, 2016. "Monetary policy shocks, set-identifying restrictions, and asset prices: A benchmarking approach for analyzing set-identified models," University of Göttingen Working Papers in Economics 295, University of Goettingen, Department of Economics.

    More about this item

    Keywords

    Structural vector autoregressive model; sign restrictions; external instruments; proxy VAR;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: 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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • 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:boe:boeewp:0961. See general information about how to correct material in RePEc.

    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: Digital Media Team (email available below). General contact details of provider: https://edirc.repec.org/data/boegvuk.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.