This online intensive course provides a comprehensive introduction to time series analysis and forecasting with EViews. The course offers a full overview on time series models and forecasting methods, covering a variety of different models including ARMA, ARDL, Regime Switching models, GARCH models and Midas time series regressions. Each session briefly introduces the different methodologies, discussing strengths and weaknesses with a focus on the interpretation of the results.
Taking a “learning-by-doing” approach, we present the most relevant time series models employing plenty of financial and macroeconomic data examples. The course specifically focuses on forecasting methodologies in macro econometrics and financial econometrics. Participants leave with the know-how on a wide range of time series 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. By the end of the two-day on line course participants should be able to:
- Model and forecast from a univariate AR(FI)MA model
- Model and forecast from a univariate GARCH (including EGARCH, TARCH, APARCH and GJR models)
- Distinguish between stationary and nonstationary series and understand the implications of using nonstationary series;
- Build, estimate and forecast from univariate time series models using Eviews an compare the forecasting performances of the models
- Understand and critically evaluate recent research in time series
- Getting started with time series data: visualization and testing.
- Unit roots tests, types of non –stationarity.
- Assessing the memory of economic and financial data over time: the auto covariance and autocorrelation functions.
- Pure time series models of the mean: AR, MA, ARMA, ARFIMA models: introduction, dependence structure and the Box Jenkins methodology to choose the best model.
- Estimation of AR(I)(FI)MA models in EViews.
- Post estimation diagnostic tests: is the model good enough? How to improve it.
- Forecasting with ARMA models.
- Adding exogenous variables to ARMA models: the ARDL framework.
- Pure time series models of the variance: the GARCH models.
- GARCH, EGARCH, TARCH, APARCH: estimation and post diagnostic tests in Stata.
- Do we really need a GARCH?
- The ARCH test.
- The ARMA-GARCH framework.
- Advanced time series models.
- Regime switching models.
- TAR models and Markov switching models.
- Estimation, interpretation and diagnostic tests in EViews.
- The Midas Model : working in time series with mixed frequency data to improve forecasting.
No previous knowledge of Eviews is required, Knowledge of linear regression and hypothesis testing is desirable.
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.
- Delegates are provided with temporary licences for the principal software package(s) used in the delivery of the course. It is essential that these temporary training licenses are installed on your computers prior to the start of the course.
- Payment of course fees required prior to the course start date.
- Registration closes 1 calendar day prior to the start of the course.
- 100% fee returned for cancellations made more than 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 attendees is restricted. Please register early to guarantee your place.