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Vector Autoregressive Models Using “R”

Author

Listed:
  • Ciprian ALEXANDRU

    (Ecological University of Bucharest)

  • Nicoleta CARAGEA

    (National Institute of Statistics, Bucharest)

  • Ana Maria DOBRE

    (National Institute of Statistics, Bucharest)

Abstract

Multivariate data analysis in the context of autoregressive models has evolved as a standard instrument in econometrics. In present, there are developed packages available in R for estimating time series models; one of the most useful package is vars (Pfaff, 2008) containing functions for diagnostic testing, estimation of a restricted models, prediction, causality analysis, impulse response analysis and forecast error variance decomposition. Using the examples provided in the vars vignette, the authors tried to obtain results for the different methods and functions on the base of macroeconomic data set for Romania.

Suggested Citation

  • Ciprian ALEXANDRU & Nicoleta CARAGEA & Ana Maria DOBRE, 2013. "Vector Autoregressive Models Using “R”," SEA - Practical Application of Science, Romanian Foundation for Business Intelligence, Editorial Department, issue 1, pages 59-67, June.
    • Handle: RePEc:cmj:seapas:y:2013:i:1:alexandruc,caragean,dobream
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    References listed on IDEAS

    as
    1. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    2. 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.
    3. 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.
    4. Caragea, Nicoleta & Alexandru, Ciprian Antoniade & Dobre, Ana Maria, 2012. "Bringing New Opportunities to Develop Statistical Software and Data Analysis Tools in Romania," MPRA Paper 48772, University Library of Munich, Germany.
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    Cited by:

    1. Cristian-Florin Dananau, 2015. "Non-governmental credit in Romania: a VECM-based approach," Romanian Statistical Review, Romanian Statistical Review, vol. 63(1), pages 87-106, March.
    2. Madalina-Gabriela ANGHEL & Aurelian DIACONU, 2016. "Equilibrium and auto regression models used for macroeconomic prognosis," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(7), pages 70-78, July.

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    More about this item

    Keywords

    Autoregressive models; Testing; R; vars;
    All these keywords.

    JEL classification:

    • 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
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets

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