John Michael, Riveros-Gavilanes (2025): Metodología estándar de vectores autoregresivos (VAR) y de corrección del error (VEC).
![]() |
PDF
MPRA_paper_123983.pdf Download (865kB) |
Abstract
English: 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: 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.
Item Type: | MPRA Paper |
---|---|
Original Title: | Metodología estándar de vectores autoregresivos (VAR) y de corrección del error (VEC) |
English Title: | Standard methodology of vector autoregression (VAR) and error correction (VEC) |
Language: | Spanish |
Keywords: | Vector autoregresion; cointegracion; estacionariedad; series de tiempo |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C10 - General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models |
Item ID: | 124015 |
Depositing User: | John Michael Riveros Gavilanes |
Date Deposited: | 19 Mar 2025 16:53 |
Last Modified: | 19 Mar 2025 16:53 |
References: | Engle, R. F., & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251–276. https://doi.org/10.2307/1913236 Hamilton, J. D. (1994). Time Series Analysis | Princeton University Press (1st ed.). Princetone New Jersey. https://press.princeton.edu/books/hardcover/9780691042893/time-series-analysis Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2), 231–254. https://doi.org/10.1016/0165-1889(88)90041-3 Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 59(6), 1551–1580. https://doi.org/10.2307/2938278 Johansen, S., & Juselius, K. (1990). Maximum Likelihood Estimation and Inference on Cointegration—With Applications to the Demand for Money. Oxford Bulletin of Economics and Statistics, 52(2), 169–210. https://doi.org/10.1111/j.1468-0084.1990.mp52002003.x Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. https://doi.org/10.1007/978-3-540-27752-1 Mohr, F. (2019). An Introduction to Vector Error Correction Models (VECMs) · r-econometrics. https://www.r-econometrics.com/timeseries/vecintro/ Pantula, S. G. (1989). Testing for Unit Roots in Time Series Data. Econometric Theory, 5(2), 256–271. Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48. https://doi.org/10.2307/1912017 Sims, C. A. (1986). Are Forecasting Models Usable for Policy Analysis? | Federal Reserve Bank of Minneapolis. https://www.minneapolisfed.org/research/quarterly-review/are-forecasting-models-usable-for-policy-analysis Stock, J. H., & Watson, M. W. (2020). Introduction to econometrics (Fourth, global edition.). Pearson. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/124015 |