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On weak identification in structural VARMA models

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  • Yao, Wenying
  • Kam, Timothy
  • Vahid, Farshid

Abstract

We simulate synthetic data from known data generating processes (DGPs) that arise from economic theory, and compare the performance of fitted VAR and VARMA models in estimating the true impulse responses to structural shocks. We show that while the VARMA structures implied by these DGPs are theoretically identified and lead to precise estimates of impulse responses given enough data, their parameters are close to the non-identified ridge in the parameter space, and that makes precise estimation of the impulse responses in small samples typical of macroeconomic data improbable. As a result, VARMA models barely show any advantage over VARs in characterizing the known DGPs in small samples. This is a refinement of the conjecture that near non-stationarity, near non-invertibility or weak identification could be possible reasons for the failure of structural VARMA models in providing good estimates of theoretical impulse responses of particular DSGE models.

Suggested Citation

  • Yao, Wenying & Kam, Timothy & Vahid, Farshid, 2017. "On weak identification in structural VARMA models," Economics Letters, Elsevier, vol. 156(C), pages 1-6.
    • Handle: RePEc:eee:ecolet:v:156:y:2017:i:c:p:1-6
    DOI: 10.1016/j.econlet.2017.03.035
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    1. Blanchard, Olivier Jean & Quah, Danny, 1989. "The Dynamic Effects of Aggregate Demand and Supply Disturbances," American Economic Review, American Economic Association, vol. 79(4), pages 655-673, September.
    2. Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez & Thomas J. Sargent & Mark W. Watson, 2007. "ABCs (and Ds) of Understanding VARs," American Economic Review, American Economic Association, vol. 97(3), pages 1021-1026, June.
    3. Hansen, Gary D., 1985. "Indivisible labor and the business cycle," Journal of Monetary Economics, Elsevier, vol. 16(3), pages 309-327, November.
    4. Ravenna, Federico, 2007. "Vector autoregressions and reduced form representations of DSGE models," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2048-2064, October.
    5. Kascha, Christian & Mertens, Karel, 2009. "Business cycle analysis and VARMA models," Journal of Economic Dynamics and Control, Elsevier, vol. 33(2), pages 267-282, February.
    6. King, Robert G. & Plosser, Charles I. & Rebelo, Sergio T., 1988. "Production, growth and business cycles : II. New directions," Journal of Monetary Economics, Elsevier, vol. 21(2-3), pages 309-341.
    7. Pagan, A.R. & Pesaran, M. Hashem, 2008. "Econometric analysis of structural systems with permanent and transitory shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3376-3395, October.
    8. 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.
    9. Christopher J. Erceg & Luca Guerrieri & Christopher Gust, 2005. "Can Long-Run Restrictions Identify Technology Shocks?," Journal of the European Economic Association, MIT Press, vol. 3(6), pages 1237-1278, December.
    10. Bagliano, Fabio C. & Favero, Carlo A., 1998. "Measuring monetary policy with VAR models: An evaluation," European Economic Review, Elsevier, vol. 42(6), pages 1069-1112, June.
    11. George Athanasopoulos & Farshid Vahid, 2008. "A complete VARMA modelling methodology based on scalar components," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(3), pages 533-554, May.
    12. Chari, V.V. & Kehoe, Patrick J. & McGrattan, Ellen R., 2008. "Are structural VARs with long-run restrictions useful in developing business cycle theory?," Journal of Monetary Economics, Elsevier, vol. 55(8), pages 1337-1352, November.
    13. Cogley, Timothy & Nason, James M., 1993. "Impulse dynamics and propagation mechanisms in a real business cycle model," Economics Letters, Elsevier, vol. 43(1), pages 77-81.
    14. Cooley, Thomas F. & Dwyer, Mark, 1998. "Business cycle analysis without much theory A look at structural VARs," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 57-88.
    15. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 527-724, Elsevier.
    16. 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.
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    Cited by:

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    More about this item

    Keywords

    VARMA; VAR; DSGE; Impulse response analysis;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • 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

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