This two-day course will explore two important topics in Econometrics; Panel Data estimation and the use of factor models in economic forecasting and analysis.
This course will acquaint the student with modern panel data techniques including their use for standard stationary panels, dynamic panels and the broad area of non stationary panels. By the end of the two day course the participants should be able to; understand the structure of a panel data set and know the two ways to define such data sets in E-VIEWS. Estimate fixed and random effect models and construct Hausman tests between the two formulations. Conduct standard hypothesis tests. Understand the importance of stationarity for panels and use panel stationarity test. Test cointegration for a panel.
Factor analysis allows us to concentrate the important information contained in a large number of data series into a relatively small number of artificial factors which may be used for various purposes. Factor analysis begins with the single factor model which is estimated in state space form using the Kalman filter. It progresses to the multifactor models using principal components. It then combines these two into the dynamic facto model. These techniques are becoming increasingly important as we move into a world of ‘Big’ data.
This course provides a comprehensive introduction to panel data econometrics in EViews - the most powerful and user-friendly econometric software. Taking a “learning-by-doing” approach, we aim to present the most relevant static and dynamic panel data models and related estimation methods (such as fixed effect, random effect GLS,GMM) by employing plenty of examples and a constant stream of challenging exercises.
The course specifically focuses on application of traditional panel techniques to micro and macro panel data set. Participants leave with comprehensive know-how on a wide range of models alongside the ability to identify which one to use for a specific research or policy question. Some more advanced topics, including serial correlation, cointegration and stationarity, are illustrated according to the need of the participants.
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 course, all participants will feel comfortable with the following tasks:
- Panel and pools
- Creating pools ( pools objects and pools workfiles)
- Working with pool data (statistic and analysis)
- Pool data models and estimation( fixed effects, random effects, robust standard errors)
- Post estimation diagnostic in pool data models
- From pools to panels : choosing the best model for your research question
- Working with panel data (trend, lag, samples, statistics)
- Analysis of panel data (unit root tests, cointegration)
- Panel data models and estimation (least squares, instrumental variables, GMM, dynamic GMM)
- Panel estimation analysis (post estimation diagnostic tests and interpretation)
Principal text for pre-course reading:
- Basic knowledge of regression modelling and introductory time series is desirable
- Basic knowledge of EViews is helpful but not required
Adkins L.C , Hill R.C Using EViews for principle of Econometrics
(Appendix C and Chapters 2 and 3 )
Principal text for post-course reading:
Adkins L.C , Hill R.C Using EViews for principle of Econometrics (Chapters 9, 12, 13,14 )
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.