Skip to main content

Vector Autoregressions II: Extensions

  • Chapter
  • First Online:
Time Series Econometrics

Part of the book series: Springer Texts in Business and Economics ((STBE))

  • 3248 Accesses

Abstract

In the previous chapter, we covered the basics of reduced form VARs on stationary data. In this chapter, we continue learning about VARs, but we extend the discussion to structural VARs (SVARs) and VARS with integrated variables.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Dolado and Lütkepohl (1996) derived many of the same results for the simpler case that the variables are I(1). The paper by Toda and Yamamoto (1995) is more general, showing the case for variables integrated up to an arbitrary order d.

References

  • Amisano, G., & Giannini, C. (2012). Topics in structural VAR econometrics. Berlin: Springer Science and Business Media.

    Google Scholar 

  • Blanchard, O. J., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79(4), 655–673.

    Google Scholar 

  • Dolado, J. J., & Lütkepohl, H. (1996). Making Wald tests work for cointegrated VAR systems. Econometric Reviews, 15(4), 369–386.

    Article  Google Scholar 

  • Enders, W. (2014). Applied econometric time series (3rd edn.). New York: Wiley.

    Google Scholar 

  • Granger, C. W. (2004). Time series analysis, cointegration, and applications. American Economic Review, 94, 421–425.

    Article  Google Scholar 

  • Kilian, L., & Lütkepohl, H. (2017). Structural vector autoregressive analysis. Cambridge University Press.

    Google Scholar 

  • Lütkepohl, H. (2005). New introduction to multiple time series analysis. Berlin: Springer Science and Business Media.

    Book  Google Scholar 

  • Rachev, S. T., Mittnik, S., Fabozzi, F. J., Focardi, S. M., et al. (2007). Financial econometrics: From basics to advanced modeling techniques (Vol. 150). New York: Wiley.

    Google Scholar 

  • Schenck, D. (2016). Log-run restrictions in a structural vector autoregression. https://blog.stata.com/2016/10/27/long-run-restrictions-in-a-structural-vector-autoregression/.

    Google Scholar 

  • Shumway, R. H., & Stoffer, D. S. (2006). Time series analysis and its applications: With R examples. New York: Springer Science and Business Media.

    Google Scholar 

  • Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1), 225–250.

    Article  Google Scholar 

  • Tsay, R. S. (2013). Multivariate time series analysis: With R and financial applications. New York: Wiley.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Levendis, J.D. (2018). Vector Autoregressions II: Extensions. In: Time Series Econometrics. Springer Texts in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-98282-3_11

Download citation

Publish with us

Policies and ethics