Vector Autoregression (VAR) is used to capture the linear interdependencies among multiple time series.
The course will cover: stationary VARs, starting from the basics and tackling more advanced techniques such as dealing with over-parameterisation via Bayesian estimation; non stationary VARs and Johansen approach to cointegration; and structural VARs, and what can be done in EViews 9, will also be explored.
Click here to view the full course agenda.
Day 1 - Stationary VARs
Session 1: Stationary VARs (Part 1)
- VAR representation and estimation
Session 2: Stationary VARs (Part 2)
- Testing with multivariate regression
- Granger causality
- Lag selection
- Misspecification tests
- VAR forecasting
Session 3: Bayesian VARs (Part 1)
- Introductory notions on priors and shrinkage
- The BVAR object in EViews
Session 4: Bayesian VARs (Part 2)
- Further discussion of priors; exercises on stationary VARS and BVARS
Day 2 - Non-Stationary VARs
Session 1: Non-Stationary VARs (Part 1)
- Cointegrated VARS in EViews: Johansen’s test for cointegration
Session 2: Non-Stationary VARs (Part 2)
- The Vector (Error) Correction Model (VECM)
- Estimating and interpreting a VECM in EViews
Session 3: Non-Stationary VARs (Part 3)
- Granger causality analysis in cointegrated VAR
Session 4: More on the impulse response function
- Worked examples using VARS, non-stationary VARS and BVARS
Day 3 - Structural VARs and foundations of time-varying VARs
Session 1: Structural VARs (Part 1)
- Structural restrictions: syntax and preliminary information
Session 2: Structural VARs (Part 2)
- Short-run restrictions: theory, obtaining response to shocks and exercises
Session 3: Structural VARs (Part 3)
- Long-run restrictions: theory, obtaining response to shocks and exercises
Session 4: Time varying VARs
- An introduction to the time-varying parameter model in EViews; specifying a multivariate time varying parameter model
Principal texts for pre-course reading
- Hamilton, J.D., 1994. Time Series Analysis. Princeton University Press.
Principal texts for post-course reading
- Pesaran, M.H., 2015. Time Series and Panel Data Econometrics. Oxford University Press.
Subject to minor changes
- Econometrics knowledge - Knowledge of basic econometrics (at a minimum: time series regression, least squares estimation, mis-specification testing, univariate cointegration).
- Software knowledge - In terms of software knowledge: basic knowledge of EViews (any version, although version 9 is preferable).
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, lunch and refreshments.
- Attendees 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. Alternatively, we can also provide laptops for an additional cost of £12.00 per day.
- If you need assistance in locating hotel accommodation, please notify us at the time of booking.
- Payment of course fees required prior to the course start date.
- Registration closes 5-calendar days prior to the start of the course.
- 100% fee returned for cancellations made over 28-calendar days prior to start of the course.
- 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
- No fee returned for cancellations made less than 14-calendar days prior to the start of the course.
The number of seats available is restricted. Please register early to guarantee your place.