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Metodología estándar de vectores autoregresivos (VAR) y de corrección del error (VEC)
[Standard methodology of vector autoregression (VAR) and error correction (VEC)]

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  • John Michael, Riveros-Gavilanes

Abstract

English version This document provides a practical introduction to the standard methodology for estimating Vector Autoregression (VAR) models and their Vector Error Correction (VEC) approach in the context of cointegration. It covers basic concepts such as stationarity and unit roots, unit root testing, cointegration analysis, and the general estimation framework using Stata. The text does not delve into the mathematical formalization of the models but rather aims to serve as an applied estimation guide for undergraduate students. Spanish version Este documento presenta una introducción practica a la metodología estándar de la estimación de vectores auto-regresivos (VAR) y su aproximación de vectores con corrección del error (VEC) en el contexto de la cointegración. El documento presenta unas nociones básicas sobre el concepto de estacionariedad y raíz unitaria, la estimación de pruebas de raíces unitarias, la revisión de cointegración y el esquema general de estimación bajo el programa Stata. El texto no ahonda con la profundización matemática de los modelos sino más que nada aspira a ser una guía aplicada de estimación para los estudiantes de pregrado.

Suggested Citation

  • John Michael, Riveros-Gavilanes, 2025. "Metodología estándar de vectores autoregresivos (VAR) y de corrección del error (VEC) [Standard methodology of vector autoregression (VAR) and error correction (VEC)]," MPRA Paper 124015, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:124015
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    File URL: https://mpra.ub.uni-muenchen.de/124015/1/MPRA_paper_123983.pdf
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    References listed on IDEAS

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    1. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
    2. Robert Engle & Clive Granger, 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.
    3. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-1580, November.
    4. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    5. Christopher A. Sims, 1986. "Are forecasting models usable for policy analysis?," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 10(Win), pages 2-16.
    6. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    7. Pantula, Sastry G., 1989. "Testing for Unit Roots in Time Series Data," Econometric Theory, Cambridge University Press, vol. 5(2), pages 256-271, August.
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    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • 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

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