IDEAS home Printed from https://ideas.repec.org/p/fip/fedcwp/1528.html
   My bibliography  Save this paper

Identifying Structural VARs with a Proxy Variable and a Test for a Weak Proxy

Author

Listed:
  • Kurt Graden Lunsford

Abstract

This paper develops a simple estimator to identify structural shocks in vector autoregressions (VARs) by using a proxy variable that is correlated with the structural shock of interest but uncorrelated with other structural shocks. When the proxy variable is weak, modeled as local to zero, the estimator is inconsistent and converges to a distribution. This limiting distribution is characterized, and the estimator is shown to be asymptotically biased when the proxy variable is weak. The F statistic from the projection of the proxy variable onto the VAR errors can be used to test for a weak proxy variable, and the critical values for different VAR dimensions, levels of asymptotic bias, and levels of statistical significance are provided. An important feature of this F statistic is that its asymptotic distribution does not depend on parameters that need to be estimated.

Suggested Citation

  • Kurt Graden Lunsford, 2015. "Identifying Structural VARs with a Proxy Variable and a Test for a Weak Proxy," Working Papers (Old Series) 1528, Federal Reserve Bank of Cleveland.
    • Handle: RePEc:fip:fedcwp:1528
    DOI: 10.26509/frbc-wp-201528
    as

    Download full text from publisher

    File URL: https://doi.org/10.26509/frbc-wp-201528
    File Function: Persistent link
    Download Restriction: no
    File URL: https://www.clevelandfed.org/-/media/project/clevelandfedtenant/clevelandfedsite/publications/working-papers/2015/wp-1528-identifying-structural-vars-pdf.pdf
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.26509/frbc-wp-201528?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Brüggemann, Ralf & Jentsch, Carsten & Trenkler, Carsten, 2016. "Inference in VARs with conditional heteroskedasticity of unknown form," Journal of Econometrics, Elsevier, vol. 191(1), pages 69-85.
    3. 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.
    4. Nicholas Bloom, 2009. "The Impact of Uncertainty Shocks," Econometrica, Econometric Society, vol. 77(3), pages 623-685, May.
    5. Kliem, Martin & Kriwoluzky, Alexander, 2013. "Reconciling narrative monetary policy disturbances with structural VAR model shocks?," Economics Letters, Elsevier, vol. 121(2), pages 247-251.
    6. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    7. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    8. Stock, James H & Wright, Jonathan H & Yogo, Motohiro, 2002. "A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 518-529, October.
    9. 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.
    10. 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.
    11. 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.
    12. Zheng Liu & John Fernald & Susanto Basu, 2012. "Technology Shocks in a Two-Sector DSGE Model," 2012 Meeting Papers 1017, Society for Economic Dynamics.
    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. 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.
    2. Laurent Ferrara & Luca Metelli & Filippo Natoli & Daniele Siena, 2020. "Questioning the puzzle: Fiscal policy, exchange rate and inflation," Working papers 752, Banque de France.
    3. Ettmeier, Stephanie & Kriwoluzky, Alexander, 2019. "Same, but different? Testing monetary policy shock measures," Economics Letters, Elsevier, vol. 184(C).
    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. Giacomini, Raffaella & Kitagawa, Toru & Read, Matthew, 2022. "Robust Bayesian inference in proxy SVARs," Journal of Econometrics, Elsevier, vol. 228(1), pages 107-126.
    6. 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.
    7. Aeimit Lakdawala, 2019. "Decomposing the effects of monetary policy using an external instruments SVAR," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 934-950, September.
    8. Michael Ryan, 2020. "A Narrative Approach to Creating Instruments with Unstructured and Voluminous Text: An Application to Policy Uncertainty," Working Papers in Economics 20/10, University of Waikato.
    9. Carsten Jentsch & Kurt G. Lunsford, 2022. "Asymptotically Valid Bootstrap Inference for Proxy SVARs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1876-1891, October.
    10. 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).
    11. Andrew Keinsley & Shu Wu, 2020. "Marginal Income Tax Rates, Economic Growth, and Primary Fiscal Deficits," Public Finance Review, , vol. 48(5), pages 676-705, September.
    12. 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.
    13. 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.
    14. G. Angelini & L. Fanelli, 2018. "Identification and estimation issues in Structural Vector Autoregressions with external instruments," Working Papers wp1122, Dipartimento Scienze Economiche, Universita' di Bologna.

    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. Carsen Jentsch & Kurt Graden Lunsford, 2016. "Proxy SVARs: Asymptotic Theory, Bootstrap Inference, and the Effects of Income Tax Changes in the United States," Working Papers (Old Series) 1619, Federal Reserve Bank of Cleveland.
    2. Michele Piffer & Maximilian Podstawski, 2018. "Identifying Uncertainty Shocks Using the Price of Gold," Economic Journal, Royal Economic Society, vol. 128(616), pages 3266-3284, December.
    3. Eminidou, Snezana & Zachariadis, Marios, 2022. "Firms’ expectations and monetary policy shocks in the euro area," Journal of International Money and Finance, Elsevier, vol. 122(C).
    4. Ricco, Giovanni & Callegari, Giovanni & Cimadomo, Jacopo, 2014. "Signals from the Government: Policy Uncertainty and the Transmission of Fiscal Shocks," MPRA Paper 56136, University Library of Munich, Germany.
    5. 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.
    6. 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.
    7. 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).
    8. Giacomini, Raffaella & Kitagawa, Toru & Read, Matthew, 2022. "Robust Bayesian inference in proxy SVARs," Journal of Econometrics, Elsevier, vol. 228(1), pages 107-126.
    9. Kim, Wongi, 2019. "Government spending policy uncertainty and economic activity: US time series evidence," Journal of Macroeconomics, Elsevier, vol. 61(C), pages 1-1.
    10. G. Angelini & L. Fanelli, 2018. "Identification and estimation issues in Structural Vector Autoregressions with external instruments," Working Papers wp1122, Dipartimento Scienze Economiche, Universita' di Bologna.
    11. Giovanni Caggiano & Efrem Castelnuovo & Gabriela Nodari, 2014. "Uncertainty and Monetary Policy in Good and Bad Times," "Marco Fanno" Working Papers 0188, Dipartimento di Scienze Economiche "Marco Fanno".
    12. Martin Bruns & Helmut Lütkepohl, 2023. "An Alternative Bootstrap for Proxy Vector Autoregressions," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1857-1882, December.
    13. Michael Ryan, 2020. "A Narrative Approach to Creating Instruments with Unstructured and Voluminous Text: An Application to Policy Uncertainty," Working Papers in Economics 20/10, University of Waikato.
    14. Husted, Lucas & Rogers, John & Sun, Bo, 2020. "Monetary policy uncertainty," Journal of Monetary Economics, Elsevier, vol. 115(C), pages 20-36.
    15. 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.
    16. 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).
    17. Ricardo Nunes & Ali Ozdagli & Jenny Tang, 2022. "Interest Rate Surprises: A Tale of Two Shocks," Working Papers 2213, Federal Reserve Bank of Dallas.
    18. 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.
    19. Pierluigi Balduzzi & Emanuele Brancati & Marco Brianti & Fabio Schiantarelli, 2019. "Populism, Political Risk and the Economy: Lessons from Italy," Boston College Working Papers in Economics 989, Boston College Department of Economics, revised 28 Apr 2020.
    20. 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.

    More about this item

    Keywords

    F Statistic; Productivity Shocks; proxy variables; Structural Vector Autoregression; total factor productivity; Weak IV;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: 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
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

    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:fip:fedcwp:1528. 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: 4D Library (email available below). General contact details of provider: https://edirc.repec.org/data/frbclus.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.