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Alternative procedures for estimating vector autoregressions identified with long-run restrictions

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Abstract

We show that the standard procedure for estimating long-run identified vector autoregressions uses a particular estimator of the zero-frequency spectral density matrix of the data. We develop alternatives to the standard procedure and evaluate the properties of these alternative procedures using Monte Carlo experiments in which data are generated from estimated real business cycle models. We focus on the properties of estimated impulse response functions. In our examples, the alternative procedures have better small sample properties than the standard procedure, with smaller bias, smaller mean square error and better coverage rates for estimated confidence intervals.

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  • Lawrence J. Christiano & Martin S. Eichenbaum & Robert J. Vigfusson, 2005. "Alternative procedures for estimating vector autoregressions identified with long-run restrictions," International Finance Discussion Papers 842, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgif:842
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    1. Jordi Gali, 1999. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," American Economic Review, American Economic Association, vol. 89(1), pages 249-271, March.
    2. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2007. "Assessing Structural VARs," NBER Chapters, in: NBER Macroeconomics Annual 2006, Volume 21, pages 1-106, National Bureau of Economic Research, Inc.
    3. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2004. "A Critique of Structural VARs Using Real Business Cycle Theory," Levine's Bibliography 122247000000000518, UCLA Department of Economics.
    4. Andrews, Donald W K & Monahan, J Christopher, 1992. "An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator," Econometrica, Econometric Society, vol. 60(4), pages 953-966, July.
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    5. 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.
    6. Charles, Amélie & Darné, Olivier & Tripier, Fabien, 2015. "Are Unit Root Tests Useful In The Debate Over The (Non)Stationarity Of Hours Worked?," Macroeconomic Dynamics, Cambridge University Press, vol. 19(1), pages 167-188, January.
    7. Dupor, Bill & Han, Jing & Tsai, Yi-Chan, 2009. "What do technology shocks tell us about the New Keynesian paradigm?," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 560-569, May.
    8. Rossi, Lorenza, 2019. "The overshooting of firms’ destruction, banks and productivity shocks," European Economic Review, Elsevier, vol. 113(C), pages 136-155.
    9. John W. Keating, 2013. "Interpreting Permanent Shocks to Output When Aggregate Demand May Not Be Neutral in the Long Run," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(4), pages 747-756, June.
    10. Mertens, Elmar, 2012. "Are spectral estimators useful for long-run restrictions in SVARs?," Journal of Economic Dynamics and Control, Elsevier, vol. 36(12), pages 1831-1844.
    11. Dieppe, Alistair & Francis, Neville & Kindberg-Hanlon, Gene, 2021. "The identification of dominant macroeconomic drivers: coping with confounding shocks," Working Paper Series 2534, European Central Bank.
    12. Sean Holly & Ivan Petrella, 2008. "Factor demand linkages and the business cycle: interpreting aggregate fluctuations as sectoral fluctuations," CDMA Conference Paper Series 0809, Centre for Dynamic Macroeconomic Analysis.
    13. 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.
    14. Ufuk Devrim Demirel, 2015. "Identification of technology shocks using misspecified VARs," Canadian Journal of Economics, Canadian Economics Association, vol. 48(4), pages 1321-1349, November.
    15. Lorenza Rossi, 2016. "Productivity Shocks and Uncertainty Shocks in a Model with Endogenous Firms Exit and Inefficient Banks," DEM Working Papers Series 128, University of Pavia, Department of Economics and Management.
    16. Christian Gourieroux & Joann Jasiak, 2022. "Long Run Risk in Stationary Structural Vector Autoregressive Models," Papers 2202.09473, arXiv.org.
    17. Guay, Alain & Pelgrin, Florian, 2023. "Structural VAR models in the Frequency Domain," Journal of Econometrics, Elsevier, vol. 236(1).
    18. Hjalmarsson, Erik, 2007. "Fully modified estimation with nearly integrated regressors," Finance Research Letters, Elsevier, vol. 4(2), pages 92-94, June.
    19. John W. Keating, 2013. "Interpreting Permanent Shocks to Output When Aggregate Demand May Not Be Neutral in the Long Run," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(4), pages 747-756, June.
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    More about this item

    Keywords

    Vector analysis; Vector autoregression; Econometric models;
    All these keywords.

    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights

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