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Inference in Nearly Nonstationary SVAR Models With Long-Run Identifying Restrictions

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  • Gospodinov, Nikolay

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  • Gospodinov, Nikolay, 2010. "Inference in Nearly Nonstationary SVAR Models With Long-Run Identifying Restrictions," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 1-12.
    • Handle: RePEc:bes:jnlbes:v:28:i:1:y:2010:p:1-12
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    Cited by:

    1. Patrick Fève & Alain Guay, 2010. "Identification of Technology Shocks in Structural Vars," Economic Journal, Royal Economic Society, vol. 120(549), pages 1284-1318, December.
    2. P. A. Nazarov & Kazakova, Maria, 2014. "Theoretical Basis of Prediction of Main Budget Parameters of Country," Published Papers r90221, Russian Presidential Academy of National Economy and Public Administration.
    3. P. A. Nazarov & Kazakova, Maria, 2014. "Development of Prediction Model of Basic Budget Parameters in Russian Federation," Published Papers r90220, Russian Presidential Academy of National Economy and Public Administration.
    4. Chevillon, Guillaume & Mavroeidis, Sophocles & Zhan, Zhaoguo, 2016. "Robust inference in structural VARs with long-run restrictions," ESSEC Working Papers WP1702, ESSEC Research Center, ESSEC Business School.
    5. Stanislav Anatolyev & Nikolay Gospodinov, 2012. "Asymptotics of near unit roots (in Russian)," Quantile, Quantile, issue 10, pages 57-71, December.
    6. Chaudourne, Jeremy & Fève, Patrick & Guay, Alain, 2014. "Understanding the effect of technology shocks in SVARs with long-run restrictions," Journal of Economic Dynamics and Control, Elsevier, vol. 41(C), pages 154-172.
    7. Helmut Lütkepohl & Anna Staszewska-Bystrova & Peter Winker, 2018. "Estimation of structural impulse responses: short-run versus long-run identifying restrictions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(2), pages 229-244, April.
    8. Patrick Fève & Alain Guay, 2009. "The Response of Hours to a Technology Shock: A Two-Step Structural VAR Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(5), pages 987-1013, August.
    9. Velinov, Anton, 2016. "On the importance of testing structural identification schemes and the potential consequences of incorrectly identified models," VfS Annual Conference 2016 (Augsburg): Demographic Change 145581, Verein für Socialpolitik / German Economic Association.
    10. Tom Holden, 2010. "Products, patents and productivity persistence: A DSGE model of endogenous growth," Economics Series Working Papers 512, University of Oxford, Department of Economics.
    11. Christopher J. Gust & Robert J. Vigfusson, 2009. "The power of long-run structural VARs," International Finance Discussion Papers 978, Board of Governors of the Federal Reserve System (U.S.).
    12. Markku Lanne & Henri Nyberg, 2015. "Nonlinear dynamic interrelationships between real activity and stock returns," CREATES Research Papers 2015-36, Department of Economics and Business Economics, Aarhus University.
    13. Andrei Polbin & Sergey Drobyshevsky, 2014. "Developing a Dynamic Stochastic Model of General Equilibrium for the Russian Economy," Research Paper Series, Gaidar Institute for Economic Policy, issue 166P, pages 156-156.
    14. Velinov, Anton & Chen, Wenjuan, 2015. "Do stock prices reflect their fundamentals? New evidence in the aftermath of the financial crisis," Journal of Economics and Business, Elsevier, vol. 80(C), pages 1-20.
    15. Nikolay Gospodinov & Alex Maynard & Elena Pesavento, 2011. "Sensitivity of Impulse Responses to Small Low-Frequency Comovements: Reconciling the Evidence on the Effects of Technology Shocks," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(4), pages 455-467, October.
    16. Lenard Lieb & Stephan Smeekes, 2017. "Inference for Impulse Responses under Model Uncertainty," Papers 1709.09583, arXiv.org, revised Oct 2019.
    17. P. A. Nazarov & Kazakova, Maria, 2014. "Methodological Principles of Prediction of Tax Revenues of Budgetary System," Published Papers r90219, Russian Presidential Academy of National Economy and Public Administration.
    18. 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.
    19. Sevgi Coskun, 2020. "Technology Shocks and Non-stationary Hours in Emerging Countries and DSVAR," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 14(2), pages 129-163, May.
    20. Lutz Kilian, 2013. "Structural vector autoregressions," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 22, pages 515-554, Edward Elgar Publishing.
    21. Nguyen, Trang & Chaiechi, Taha & Eagle, Lynne & Low, David, 2020. "Dynamic impacts of SME stock market development and innovation on macroeconomic indicators: A Post-Keynesian approach," Economic Analysis and Policy, Elsevier, vol. 68(C), pages 327-347.

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