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Vector autoregressions and cointegration

Author

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  • Mark W. Watson
Abstract
This paper surveys three topics: vector autoregressive (VAR) models with integrated regressors, cointegration, and structural VAR modeling. The paper begins by developing methods to study potential "unit root" problems in multivariate models, and then presents a simple set of rules designed to help applied researchers conduct inference in VARs. A large number of examples are studied, including tests for Granger causality, tests for VAR lag length, spurious regressions and OLS estimators of cointegrating vectors. The survey of cointegration begins with four alternative representations of cointegrated systems: the vector error correction model (VECM), and the moving average, common trends and triangular representations. A variety of tests for cointegration and efficient estimators for cointegrating vectors are developed and compared. Finally, structural VAR modeling is surveyed, with an emphasis on interpretation, econometric identification and construction of efficient estimators. Each section of this survey is largely self-contained. Inference in VARs with integrated regressors is covered in Section 2, cointegration is surveyed in Section 3, and structural VAR modeling is the subject of Section 4.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Mark W. Watson, 1993. "Vector autoregressions and cointegration," Working Paper Series, Macroeconomic Issues 93-14, Federal Reserve Bank of Chicago.
    • Handle: RePEc:fip:fedhma:93-14
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    Cited by:

    1. Pierre St-Amant, 1996. "Decomposing U.S. Nominal Interest Rates into Expected Inflation and Ex Ante Real Interest rates Using Structural VAR Methodology," Staff Working Papers 96-2, Bank of Canada.
    2. Dupasquier, Chantal & Guay, Alain & St-Amant, Pierre, 1999. "A Survey of Alternative Methodologies for Estimating Potential Output and the Output Gap," Journal of Macroeconomics, Elsevier, vol. 21(3), pages 577-595, July.
    3. Bin Chen & Jinho Choi & Juan Carlos Escanciano, 2017. "Testing for fundamental vector moving average representations," Quantitative Economics, Econometric Society, vol. 8(1), pages 149-180, March.
    4. Ben Fung & Rohit Gupta, "undated". "Searching for the Liquidity Effect in Canada," Staff Working Papers 94-12, Bank of Canada.
    5. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2019. "Priors for the Long Run," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 565-580, April.
    6. Vegard H. Larsen & Leif Anders Thorsrud, 2015. "The Value of News," Working Papers No 6/2015, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    7. Barsky, Robert B. & Sims, Eric R., 2011. "News shocks and business cycles," Journal of Monetary Economics, Elsevier, vol. 58(3), pages 273-289.
    8. Stefan Norrbin & Kevin Reffett & Yaohua Ji, 1997. "Using a VECM to test exogeneity and forecastability in the PPP condition," Applied Financial Economics, Taylor & Francis Journals, vol. 7(1), pages 87-95.
    9. Chantal Dupasquier & Alain Guay & Pierre St-Amant, 1997. "A Comparison of Alternative Methodologies for Estimating Potential Output and the Output Gap," Staff Working Papers 97-5, Bank of Canada.

    More about this item

    Keywords

    time series analysis;

    JEL classification:

    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other

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