COURSE DATE: 19th - 21st September 2018
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.
- 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.