Our online VAR Models in EViews course offers a complete introduction to VAR and VEC models and their practical use in EViews. It provides a data oriented and practical understating of restricted and unrestricted VARs, Bayesian VARs, cointegrated VARs and VEC models and forecasting with VARs. All topics are demonstrated with macroeconomic data examples. The course targets researchers, practitioners and policy makers who are interested in gaining an in-depth knowledge of VAR models and learning how to use them in their current or future assignments.
The course runs from 13.00 - 16.00 (UK Time) / 17:00 - 20:00 (UAE Time)
The structural approach to time series modelling uses economic theory to model the relationship among the variables of interest. Unfortunately, economic theory is often not rich enough to provide a dynamic specification that identifies all of these relationships.
Furthermore, estimation and inference are complicated by the fact that endogenous variables may appear on both the left and right sides of equations. These problems lead to alternative, non-structural approaches to modelling the relationship among several variables.
This course provides a comprehensive introduction to the most popular and effective tool for non–structural modelling and forecasting: the Vector Autoregression models with EViews.
Taking a “learning-by-doing” approach, we will illustrate the restricted and unrestricted VARS, Bayesian VARs, cointegrated VARs and VECM models, along with post estimation diagnostic tools such as variance decomposition and impulse response functions, employing plenty of macroeconomic data examples and a constant stream of challenging exercises.
The course specifically focuses on VARs methodologies for macro econometrics. Participants leave with the know-how on a wide range of VAR models and the ability to identify which one to use for a specific modelling and forecasting purpose.
The course is intentionally flexible. The agenda emerges dynamically and depends on the group’s prior background and knowledge of EViews. A number of advanced topics can be discussed according to the participants’ needs, including Markov Switching VARs and MIDAS VARSs.
By the end of the course, all participants will feel comfortable with the following tasks:
- VARs basic structure: MLE estimation and specifications
- Restricted augmented VARs
- Testing Granger causality
- Bayesian VARs (Minnesota prior, Normal Wishart prior, pseudo data and dummy priors)
- Structural VARs (Representation, estimation with MLE and IV, sign restrictions)
- Impulse Response Functions
- Variance and Variable Decomposition Cointegrated VARs and the VEC model
- Forecasting VARs
- 80% practical / 20% theory
Principal texts for pre-course reading
- Adkins L.C , Hill R.C Using EViews for principle of Econometrics (Appendix C and Chapters 2 and 3)
Principal texts for post-course reading
- Adkins L.C , Hill R.C Using EViews for principle of Econometrics (Chapters 9, 12, 13, 14)
- Basic knowledge of regression modelling and introductory time series is desirable.
- Basic knowledge of EViews.
Terms & Conditions
- Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrolment).
- Additional discounts are available for multiple registrations.
- Cost includes course materials that will be posted to you prior to the start of the course.
- Delegates are provided with temporary licences for the software(s) used in the course and will be instructed to download and install the software prior to the start of the course.
- Payment of course fees required prior to the course start date.
- Registration closes 1-day prior to the start of the course.