EViews learning resources and training courses
This page provides information about updates about available learning resources and upcoming EViews training courses.
This page provides information about updates about available learning resources and upcoming EViews training courses.
EViews online tutorials allow you to learn the basics of EViews at your own pace.
Each tutorial covers a set topic, and provides example data sets and EViews files so you can follow along.
EViews Illustrated by Richard Startz from The University of California is available as a free PDF download on the EViews website.
The book is a great place to start for anybody that is new to EViews. EViews Illustrated is filled with examples, step-by-step instructions, and screen-shot images. Written by Richard Startz, professor of Economics at the University of California, and long-time EViews user, EViews Illustrated offers a step-by-step guide to the EViews program, walking you through each step starting right at the beginning.
Topics covered include:
EViews Webinars provide a way to participate in an interactive class room environment through the convenience of your web-browser.
Public webinars are offered on a regular schedule, covering a range of topics, from introductions to EViews to advanced econometrics analysis using EViews.
For more individually tailored content, you can also arrange private webinar sessions for you and your company.
7–19 November 2022 (10 days, Virtual) — Pretoria, South Africa
Presented by the Department of Economics, University of Pretoria
This course is of an applied nature and focuses on hands-on experience in estimation, interpretation and evaluation of economic relationships. The aim is to reconcile economic theory with practice, thereby empowering delegates with analytical skills and a hands-on approach to the decision-making processes.
The course begins with a basic introduction to the concepts of regression (ordinary least squares estimation) and statistical inference. Thereafter, attention is given to the violations of the classical linear regression model that are often encountered in applied econometric work and the consequences of these violations are discussed, as well as practical ways of detecting (diagnostic testing) and solving these problems. Various applications of regression analysis, such as forecasting and policy simulation are discussed and applied. The course also includes the discussion of the practical implications of employing non-stationary data in estimation, the detection of unit roots in the underlying data-generating processes and concepts of residual-based co-integration and error-correction modelling.
What the course covers
Admission requirements
Matric or Grade 12, with exposure to empirical economic analysis. Statistics at the second-year level is advised.
Who should enrol?
Experience as a researcher or analyst in any of the following fields of economic application is required: financial markets, socio-economics and health, development economics, public finance and tax policy or international trade and finance.
Enrolment and more information
For the course structure, fees, brochure, enquiries and online enrolment, please visit the course website.
19 September–1 October 2022 (10 days, Virtual) — Pretoria, South Africa
Presented by the Department of Economics, University of Pretoria
This course addresses modelling techniques for time-series data when unit roots are present in the data. An overview of the technical characteristics of time-series data and the concept of stationarity is provided; the econometric techniques of co-integration and error correction models are revised in single equations (residual-based co-integration), with emphasis on their empirical application; and the notion of multivariate co-integration is discussed and applied.
What the course covers
Overview of residual-based co-integration
Multivariate cointegration (focus of course)
Admission requirements
This is an advanced course and requires an Honours level qualification in time-series Econometrics (including knowledge of the concepts of unit root testing and residual-based (Engle-Granger) cointegration, as these are merely included as revision). An understanding of matrix algebra is essential as well as experience as a researcher or analyst in any of the fields of economic application. Proficiency in EViews is also advised. IMPORTANT NOTE:This course overlaps with the course "Econometrics for the Practitioner" in terms of content. Unit root testing and residual-based cointegration techniques are covered in both courses. It is therefore not advised to attend both these courses.
Who should enrol?
Experience as a researcher or analyst in any of the following fields of economic application is required: financial markets, socio-economics and health, development economics, public finance and tax policy or international trade and finance.
Enrolment and more information
For the course structure, fees, brochure, enquiries and online enrolment, please visit the course website.
21 November–2 December 2022 (10 days, Virtual) — Pretoria, South Africa
Presented by the Department of Economics, University of Pretoria
In this course "panel data" refers to the pooling over a number of time periods observations on a cross-section of countries, households, firms and so forth. Panel data allows for more informative results, more variability, more degrees of freedom and more efficiency. This course is of an applied nature and focuses on hands-on experience in estimation, interpretation and evaluation of economic relationships within a panel data context.
We begin the discussion with the static linear model in a panel data setting. We start with the fixed effects (FE) model and pay attention to the least squares dummy variable (LSDV) estimator and the within transformation (within estimator). We distinguish between one-way and two-way error component models. Relevant hypothesis testing include testing for the validity of fixed effects, i.e. pooling of slope and intercept coefficients vs. only pooling the slopes. We continue the discussion by assuming a case where individual effects can be considered random factors, independently and identically distributed over cross-sections, i.e. the random effects (RE or EGLS) estimator. We also discuss making a choice between FE and RE. Relevant hypothesis testing includes testing the validity of random effects and the Hausman specification test. We also consider the problems of heteroscedasticity and autocorrelation in panel data models; testing for it and potential remedies.
We then move on to topics related to endogenous regressors, dynamic linear model specification and relevant techniques (Nickel bias and correction; instrumental variables (IV) and general method of moments (GMM) estimation). We conclude with Panel time series issues and estimation (heterogeneity in both intercept and slope coefficients, random coefficients (RC) models, mean group estimator (MGE), seemingly unrelated regression (SUR) models, and panel unit root testing and Cointegration).
What the course covers
1. Stationary panel data:
2. Non-stationary panel data
Admission requirements
Honours level qualification in time-series Econometrics (including exposure to the concepts of unit root testing and cointegration). An understanding of matrix algebra and experience as a researcher or analyst in any of the fields of economic application. Proficiency in EViews is also advised.
Who should enrol?
Researchers or analysts in any of the following fields of economic application: development economics, public finance and tax policy, socio-economics and health, financial markets, as well as international trade and finance.
Enrolment and more information
For the course structure, fees, brochure, enquiries and online enrolment, please visit the course website.
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EViews 12 released - 11 Nov 2020
EViews 11 released - 03 Apr 2019
EViews 10+ adds new features to EViews 10 - 17 Oct 2017