Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

Estimation of structural vector autoregressive models

  • Lutkepohl, Helmut (DIW Berlin and Department of Economics, Freie Universitat Berlin)
  • Received : 2017.08.01
  • Accepted : 2017.08.29
  • Published : 2017.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this survey, estimation methods for structural vector autoregressive models are presented in a systematic way. Both frequentist and Bayesian methods are considered. Depending on the model setup and type of restrictions, least squares estimation, instrumental variables estimation, method-of-moments estimation and generalized method-of-moments are considered. The methods are presented in a unified framework that enables a practitioner to find the most suitable estimation method for a given model setup and set of restrictions. It is emphasized that specifying the identifying restrictions such that they are linear restrictions on the structural parameters is helpful. Examples are provided to illustrate alternative model setups, types of restrictions and the most suitable corresponding estimation methods.

Keywords

References

  1. Amisano G and Giannini C (1997). Topics in Structural VAR Econometrics (2nd ed), Springer, Berlin.
  2. Blanchard OJ and Quah D (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79, 655-673.
  3. Breitung J, Bruggemann R, and Lutkepohl H (2004). Structural vector autoregressive modeling and impulse responses. In H. Lutkepohl and M. Kratzig (Eds), Applied Time Series Econometrics (pp. 159-196), Cambridge University Press, Cambridge.
  4. Canova F (1991). The sources of financial crisis: pre- and post-Fed evidence, International Economic Review, 32, 689-713. https://doi.org/10.2307/2527114
  5. Canova F (2007). Methods for Applied Macroeconomic Research, Princeton University Press, Princeton.
  6. Canova F and P'erez Forero FJ (2015). Estimating overidentified, nonrecursive, time-varying coefficients structural vector autoregressions. Quantitative Economics, 6, 359-384. https://doi.org/10.3982/QE305
  7. Christiano LJ, Eichenbaum M, and Evans C (1999). Monetary policy shocks: What have we learned and to what end? In JB Taylor and M Woodford (Eds), Handbook of Macroeconomics (pp. 65-148), Elsevier, Amsterdam.
  8. EViews (2000). EVievs 4.0 User's Guide, Quantitative Micro Software, Irvine.
  9. Gordon DB and Leeper EM (1994). The dynamic impacts of monetary policy: an exercise in tentative identification. Journal of Political Economy, 102, 1228-1247. https://doi.org/10.1086/261969
  10. Johansen S (1995). Likelihood-based Inference in Cointegrated Vector Autoregressive Models, Oxford University Press, Oxford.
  11. Kilian L and Lutkepohl H (2017). Structural Vector Autoregressive Analysis, Cambridge University Press, Cambridge.
  12. King RG, Plosser CI, Stock JH, and Watson MW(1991). Stochastic trends and economic fluctuations. American Economic Review, 81, 819-840.
  13. Koop G and Korobilis D (2010). Bayesian multivariate time series methods for empirical macroeconomics. Foundations and Trends in Econometrics, 3, 267-358.
  14. Kratzig M (2004). The software JMulTi. In H Lutkepohl and M Kratzig (Eds), Applied Time Series Econometrics (pp. 289-299), Cambridge University Press, Cambridge.
  15. Lanne M and Lutkepohl H (2008). Identifying monetary policy shocks via changes in volatility. Journal of Money, Credit and Banking, 40, 1131-1149.
  16. Lutkepohl H (2005). New Introduction to Multiple Time Series Analysis, Springer-Verlag, Berlin.
  17. Lutkepohl H (2013). Identifying structural vector autoregressions via changes in volatility. In TB Fomby, L Kilian and A Murphy (Eds), Advances in Econometrics (pp. 169-204), JAI Press, Greenwich.
  18. Lutkepohl H, Staszewska-Bystrova A, and Winker P (2017). Estimation of structural impulse responses: short-run versus long-run identifying restrictions, Advances in Statistical Analysis, 1-16. Retrieved from: https://link.springer.com/article/10.1007/s10182-017-0300-9
  19. Pagan AR and Pesaran MH (2008). Econometric analysis of structural systems with permanent and transitory shocks. Journal of Economic Dynamics and Control, 32, 3376-3395. https://doi.org/10.1016/j.jedc.2008.01.006
  20. Rigobon R (2003). Identification through heteroskedasticity. Review of Economics and Statistics, 85, 777-792. https://doi.org/10.1162/003465303772815727
  21. Rubio-Ramirez JF, Waggoner D, and Zha T (2010). Structural vector autoregressions: theory of identification and algorithms for inference. Review of Economic Studies, 77, 665-696. https://doi.org/10.1111/j.1467-937X.2009.00578.x
  22. Sims CA (1980). Macroeconomics and reality. Econometrica, 48, 1-48. https://doi.org/10.2307/1912017
  23. Sims CA and Zha T (1998). Bayesian methods for dynamic multivariate models. International Economic Review, 39, 949-968. https://doi.org/10.2307/2527347
  24. Sims CA and Zha T (1999). Error bands for impulse responses. Econometrica, 67, 1113-1155. https://doi.org/10.1111/1468-0262.00071
  25. Uhlig H (1994). What macroeconomists should know about unit roots: a Bayesian perspective. Econometric Theory, 10, 645-671. https://doi.org/10.1017/S0266466600008719
  26. Uhlig H (2005). What are the effects of monetary policy on output? Results from an agnostic identificationprocedure, Journal of Monetary Economics, 52, 381-419. https://doi.org/10.1016/j.jmoneco.2004.05.007

Cited by

  1. Forecasting hotel demand uncertainty using time series Bayesian VAR models pp.2044-0375, 2019, https://doi.org/10.1177/1354816618801741