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VAR, SVAR and SVEC Models: Implementation Within R Package vars

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  • Bernhard Pfaff
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    Abstract

    The structure of the package vars and its implementation of vector autoregressive, structural vector autoregressive and structural vector error correction models are explained in this paper. In addition to the three cornerstone functions VAR(), SVAR() and SVEC() for estimating such models, functions for diagnostic testing, estimation of a restricted models, prediction, causality analysis, impulse response analysis and forecast error variance decomposition are provided too. It is further possible to convert vector error correction models into their level VAR representation. The different methods and functions are elucidated by employing a macroeconomic data set for Canada. However, the focus in this writing is on the implementation part rather than the usage of the tools at hand.

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    Article provided by American Statistical Association in its journal Journal of Statistical Software.

    Volume (Year): 27 ()
    Issue (Month): i04 ()
    Pages:

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    Handle: RePEc:jss:jstsof:27:i04

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    1. Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, vol. 55(2), pages 251-76, March.
    2. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    3. Banerjee, Anindya & Dolado, Juan J. & Galbraith, John W. & Hendry, David, 1993. "Co-integration, Error Correction, and the Econometric Analysis of Non-Stationary Data," OUP Catalogue, Oxford University Press, number 9780198288107.
    4. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-72, June.
    5. Breusch, T S, 1978. "Testing for Autocorrelation in Dynamic Linear Models," Australian Economic Papers, Wiley Blackwell, vol. 17(31), pages 334-55, December.
    6. Achim Zeileis & Friedrich Leisch & Kurt Hornik & Christian Kleiber, . "strucchange: An R Package for Testing for Structural Change in Linear Regression Models," Journal of Statistical Software, American Statistical Association, vol. 7(i02).
    7. Osterwald-Lenum, Michael, 1992. "A Note with Quantiles of the Asymptotic Distribution of the Maximum Likelihood Cointegration Rank Test Statistics," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 54(3), pages 461-72, August.
    8. David Edgerton & Ghazi Shukur, 1999. "Testing autocorrelation in a system perspective testing autocorrelation," Econometric Reviews, Taylor & Francis Journals, vol. 18(4), pages 343-386.
    9. Olivier Jean Blanchard & Danny Quah, 1988. "The Dynamic Effects of Aggregate Demand and Supply Disturbance," Working papers 497, Massachusetts Institute of Technology (MIT), Department of Economics.
    10. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    11. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    12. Paul Gilbert, 2000. "A note on the computation of time series model roots," Applied Economics Letters, Taylor & Francis Journals, vol. 7(7), pages 423-424.
    13. Granger, C. W. J., 1981. "Some properties of time series data and their use in econometric model specification," Journal of Econometrics, Elsevier, vol. 16(1), pages 121-130, May.
    14. Godfrey, Leslie G, 1978. "Testing for Higher Order Serial Correlation in Regression Equations When the Regressors Include Lagged Dependent Variables," Econometrica, Econometric Society, vol. 46(6), pages 1303-10, November.
    15. Bera, Anil K. & Jarque, Carlos M., 1981. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals : Monte Carlo Evidence," Economics Letters, Elsevier, vol. 7(4), pages 313-318.
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    Cited by:
    1. Tan, Bee Wah & Tang, Chor Foon, 2011. "The dynamic relationship between private domestic investment, the user cost of capital, and economic growth in Malaysia," MPRA Paper 27964, University Library of Munich, Germany.
    2. Nikolay Robinzonov & Gerhard Tutz & Torsten Hothorn, 2012. "Boosting techniques for nonlinear time series models," AStA Advances in Statistical Analysis, Springer, vol. 96(1), pages 99-122, January.
    3. Achim Zeileis & Roger Koenker, . "Econometrics in R: Past, Present, and Future," Journal of Statistical Software, American Statistical Association, vol. 27(i01).
    4. João Sousa Andrade & Adelaide Duarte & Marta Simões, 2011. "Inequality and Growth in Portugal: a time series analysis," GEMF Working Papers 2011-11, GEMF - Faculdade de Economia, Universidade de Coimbra.

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