This course is part two of a five-part EViews training series running throughout 2024.
You will find links for all other courses in the series below:
Course 3: Models for Non-Stationarity Variables in EViews
Course 4: Volatility Models and Panel Data Models
Course 5: Models for Panel Data
This course focuses on advanced time series analysis using EViews, emphasizing ARMA and VAR models. Participants delve into ARMA model intricacies, stationarity, and unit root testing. Practical aspects include univariate forecasting with ARMA and stationary VAR models. Through hands-on exercises and real-world case studies, participants gain practical skills, preparing them to apply atheoretical models effectively in time series analysis and forecasting using EViews.
Upon the course's completion, all attendees will receive a certificate of attendance as proof of professional development.
Level: Intermediate
Learning ratio: 50% Practical; 50% Theory
Session 1: Statistical Analysis of Time Series
1.1 Definitions:
Definition and components of Autoregressive Moving Average (ARMA) models.
Box-Jenkins identification procedure for time series analysis.
Treatment of trends and seasonality.
Application of filters in time series analysis.
1.2 Stationarity and Non-Stationarity:
Review of stationarity and non-stationarity in time series.
Introduction to unit roots and their significance.
Unit root testing in EViews.
Series differencing as a technique for achieving stationarity.
Session 2: Univariate Forecasting
2.1 Forecasting with ARMA Models:
Implementation of ARMA models for univariate forecasting.
Evaluation and measurement of forecasting accuracy.
Session 3: Atheoretical Models II: Stationary VARs
3.1 VAR Representation and Estimation:
Introduction to Vector Autoregression (VAR) models.
Procedures for VAR representation and estimation in EViews.
3.2 Further Testing with Multivariate Regression:
Granger causality testing within the framework of VAR models.
Lag selection methods to enhance VAR model performance.
Session 4: Atheoretical Models III - Stationary VARs
4.1 Forecasting with VARs:
Application of VAR models for forecasting in a stationary context.
Practical considerations and techniques for VAR forecasting in EViews.
Athoeretical Models in EViews
The number of attendees is restricted. Please register early to guarantee your place.