IDEAS home Printed from https://ideas.repec.org/p/hig/wpaper/124-ec-2016.html

Graphical Interpretations of Rank Conditions For Identification of Linear Gaussian Models

Author

Listed:
  • Nikolay Arefiev

    (National Research University Higher School of Economics)

Abstract

The literature on graphical models and the literature on identi cation pursue similar goals, but do not use entirely each other's results, because represent them in di erent languages. To ease the communication between these elds, I translate the most important theorems on identi cation of linear Gaussian Simultaneous Equations Models (SEMs) and Structural Vector Autoregressions (SVARs) into the language of graphical models. I propose graphical interpretations of the rank conditions for identi cation of SEMs, of the rank condition of Rubio-Ramirez et al (2010) for identi cation of SVARs with linear and nonlinear restrictions, and of the theory of partial identi cation for SVARs.

Suggested Citation

  • Nikolay Arefiev, 2016. "Graphical Interpretations of Rank Conditions For Identification of Linear Gaussian Models," HSE Working papers WP BRP 124/EC/2016, National Research University Higher School of Economics.
  • Handle: RePEc:hig:wpaper:124/ec/2016
    as

    Download full text from publisher

    File URL: https://www.hse.ru/data/2016/02/25/1139472743/124EC2016.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2016. "Bayesian Graphical Models for STructural Vector Autoregressive Processes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(2), pages 357-386, March.
    3. 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.
    4. Matteo Fragetta & Giovanni Melina, 2013. "Identification of monetary policy in SVAR models: a data-oriented perspective," Empirical Economics, Springer, vol. 45(2), pages 831-844, October.
    5. Blanchard, Olivier Jean & Quah, Danny, 1993. "The Dynamic Effects of Aggregate Demand and Supply Disturbances: Reply," American Economic Review, American Economic Association, vol. 83(3), pages 653-658, June.
    6. Rothenberg, Thomas J, 1971. "Identification in Parametric Models," Econometrica, Econometric Society, vol. 39(3), pages 577-591, May.
    7. Bryant, Henry L. & Bessler, David A., 2011. "Proving causal relationships using observational data," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 103238, Agricultural and Applied Economics Association.
    8. Selva Demiralp & Kevin Hoover & Stephen Perez, 2014. "Still puzzling: evaluating the price puzzle in an empirically identified structural vector autoregression," Empirical Economics, Springer, vol. 46(2), pages 701-731, March.
    9. Oxley, Les & Reale, Marco & Wilson, Granville Tunnicliffe, 2009. "Constructing structural VAR models with conditional independence graphs," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(9), pages 2910-2916.
    10. Hoover, Kevin D., 2005. "Automatic Inference Of The Contemporaneous Causal Order Of A System Of Equations," Econometric Theory, Cambridge University Press, vol. 21(1), pages 69-77, February.
    11. Piyachart Phiromswad, 2014. "Measuring monetary policy with empirically grounded identifying restrictions," Empirical Economics, Springer, vol. 46(2), pages 681-699, March.
    12. Marco Reale & Granville Tunnicliffe Wilson, 2001. "Identification of vector AR models with recursive structural errors using conditional independence graphs," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 10(1), pages 49-65, January.
    Full references (including those not matched with items on IDEAS)

    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. Nikolay Arefiev, 2016. "Identification of Monetary Policy Shocks within a Svar Using Restrictions Consistent with a DSGE Model," HSE Working papers WP BRP 125/EC/2016, National Research University Higher School of Economics.
    2. Nikolay Arefiev, 2014. "A Theory Of Data-Oriented Identification With A Svar Application," HSE Working papers WP BRP 79/EC/2014, National Research University Higher School of Economics.
    3. Nikolay Arefiev & Ramis Khabibullin, 2018. "Bayesian identification of structural vector autoregression models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 49, pages 115-142.
    4. Phiromswad, Piyachart, 2015. "Measuring monetary policy with empirically grounded restrictions: An application to Thailand," Journal of Asian Economics, Elsevier, vol. 38(C), pages 104-113.
    5. Raffaella Giacomini & Toru Kitagawa, 2021. "Robust Bayesian Inference for Set‐Identified Models," Econometrica, Econometric Society, vol. 89(4), pages 1519-1556, July.
    6. Cordoni, Francesco & Dorémus, Nicolas & Moneta, Alessio, 2024. "Identification of vector autoregressive models with nonlinear contemporaneous structure," Journal of Economic Dynamics and Control, Elsevier, vol. 162(C).
    7. Piyachart Phiromswad & Takeshi Yagihashi, 2016. "Empirical identification of factor models," Empirical Economics, Springer, vol. 51(2), pages 621-658, September.
    8. Lima, Elcyon Caiado & Maka, Alexis & Céspedes, Brisne, 2008. "Monetary Policy, Inflation and the Level of Economic Activity in Brazil After the Real Plan: Stylized Facts from SVAR Models," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 62(2), October.
    9. Irina Zviadadze, 2017. "Term Structure of Consumption Risk Premia in the Cross Section of Currency Returns," Journal of Finance, American Finance Association, vol. 72(4), pages 1529-1566, August.
    10. Matteo Fragetta & Giovanni Melina, 2013. "Identification of monetary policy in SVAR models: a data-oriented perspective," Empirical Economics, Springer, vol. 45(2), pages 831-844, October.
    11. Emanuele BACCHIOCCHI, 2011. "Identification in structural VAR models with different volatility regimes," Departmental Working Papers 2011-39, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    12. Selva Demiralp & Kevin Hoover & Stephen Perez, 2014. "Still puzzling: evaluating the price puzzle in an empirically identified structural vector autoregression," Empirical Economics, Springer, vol. 46(2), pages 701-731, March.
    13. Emanuele BACCHIOCCHI & Luca FANELLI, 2012. "Identification in structural vector autoregressive models with structural changes," Departmental Working Papers 2012-16, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    14. Pedro Garcia Duarte & Kevin D. Hoover, 2012. "Observing Shocks," History of Political Economy, Duke University Press, vol. 44(5), pages 226-249, Supplemen.
    15. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian Vector Autoregressions," The Warwick Economics Research Paper Series (TWERPS) 1159, University of Warwick, Department of Economics.
    16. Neusser, Klaus, 2016. "A topological view on the identification of structural vector autoregressions," Economics Letters, Elsevier, vol. 144(C), pages 107-111.
    17. Shambaugh, Jay, 2008. "A new look at pass-through," Journal of International Money and Finance, Elsevier, vol. 27(4), pages 560-591, June.
    18. Eickmeier, Sandra & Metiu, Norbert & Prieto, Esteban, 2016. "Time-varying volatility, financial intermediation and monetary policy," Discussion Papers 46/2016, Deutsche Bundesbank.
    19. Kabundi, Alain & De Simone, Francisco Nadal, 2020. "Monetary policy and systemic risk-taking in the euro area banking sector," Economic Modelling, Elsevier, vol. 91(C), pages 736-758.
    20. Filipa Sa & Pascal Towbin & tomasz wieladek, 2011. "Low interest rates and housing booms: the role of capital inflows, monetary policy and financial innovation," Bank of England working papers 411, Bank of England.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

    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:hig:wpaper:124/ec/2016. 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: Shamil Abdulaev or Shamil Abdulaev (email available below). General contact details of provider: https://edirc.repec.org/data/hsecoru.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.