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Alternative Procedures for Estimating Vector Autoregressions Identified with Long-Run Restrictions

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Listed:
  • Lawrence J. Christiano
  • Martin Eichenbaum
  • Robert Vigfusson

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. (JEL: E24, E32, O3) (c) 2006 by the European Economic Association.

Suggested Citation

  • Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2006. "Alternative Procedures for Estimating Vector Autoregressions Identified with Long-Run Restrictions," Journal of the European Economic Association, MIT Press, vol. 4(2-3), pages 475-483, 04-05.
  • Handle: RePEc:tpr:jeurec:v:4:y:2006:i:2-3:p:475-483
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    References listed on IDEAS

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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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    Cited by:

    1. Bekaert, Geert & Hoerova, Marie & Lo Duca, Marco, 2013. "Risk, uncertainty and monetary policy," Journal of Monetary Economics, Elsevier, vol. 60(7), pages 771-788.
    2. 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(01), pages 167-188, January.
    3. 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.
    4. Gouriéroux, Christian & Monfort, Alain & Renne, Jean-Paul, 2017. "Statistical inference for independent component analysis: Application to structural VAR models," Journal of Econometrics, Elsevier, vol. 196(1), pages 111-126.
    5. repec:spr:alstar:v:102:y:2018:i:2:d:10.1007_s10182-017-0300-9 is not listed on IDEAS
    6. 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.
    7. Andrew Binning, 2013. "Underidentified SVAR models: A framework for combining short and long-run restrictions with sign-restrictions," Working Paper 2013/14, Norges Bank.
    8. 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.
    9. Hjalmarsson, Erik, 2007. "Fully modified estimation with nearly integrated regressors," Finance Research Letters, Elsevier, vol. 4(2), pages 92-94, June.
    10. 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.
    11. Franz Ruch & Stan du Plessis, 2015. "Working Paper – WP/15/05- Second-Round Effects from Food and Energy Prices- an SBVAR approach," Papers 7008, South African Reserve Bank.
    12. Lorenza Rossi, 2018. "The Overshooting of Firms Destruction, Banks and Productivity Shocks," DEM Working Papers Series 147, University of Pavia, Department of Economics and Management.
    13. Lovcha, Yuliya & Pérez Laborda, Àlex, 2016. "The Variance-Frequency Decomposition as an Instrument for VAR Identification: an Application to Technology Shocks," Working Papers 2072/261537, Universitat Rovira i Virgili, Department of Economics.
    14. 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.
    15. 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.

    More about this item

    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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