Agenda

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.

Prerequisites

Athoeretical Models in EViews

• Some prior knowledge of EViews
• Knowledge of OLS models, ARMA models and some forecasting techniques

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.