Vector Autoregression (VAR) models
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vector autoregressive models for multivariate time series
Welcome to a new EViews tutorial! By watching this video, you will learn how to estimate vector autoregression (VAR) models in eviews and interpret the results. VAR stands for vector autoregression model, and was proposed by Christopher Sims in his paper “Macroeconomics and Reality (1980)”. VAR models generalize univariate models as it allows for multiple endogenous variables. Recall that on previous tutorials, we have estimated univariate models (ARIMA). Now it’s time to estimate multivariate models. Nowadays, VAR models are widely used in the economics field.
By watching both vector autoregression tutorial videos (Part 1 & 2), you will be able to estimate VAR models and produce diverse results. For our tutorial we replicate Stock and Watson (2001) paper entitled “Vector Autoregressions”. In part 1 you will learn:
- What are vector autoregression (VAR) models?
- How to estimate vector autoregression (VAR) models
- How to select the lag length using lag length criterions
- How to conduct the Engle and Granger Causality Test
- Vector autoregression (VAR) stability conditions
- Vector Autoregression (VAR) model diagnostics
vector autoregression (VAR) models - PART 2
In “VAR models in EViews – Part 2” you will be able to learn the following topics:
- Cholesky Decomposition
- Impulse Response Functions
- Variance Decomposition
- Price puzzling results and how to overcome the puzzle
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Elevate your learning experience. You can buy the package for each of the tutorials. Each package contains the slides of the video + Workfile/Do File + Data & Support