2017 EVIEWS SUMMER SCHOOL

Date of Appearance: 
Mar 29, 2017
Duration of the course: 
Jun 19, 2017 to Jun 23, 2017
Application Deadline: 
Jun 16, 2017

2017 EVIEWS SUMMER SCHOOL

Date of Appearance: 
Mar 29, 2017
Duration of the course: 
Jun 19, 2017 to Jun 23, 2017
Application Deadline: 
Jun 16, 2017

Course Details

Summer Schools
Credits or certification after course completion: 
Certificate of attendance
Study Options: 
Full-Time

Course Fees

Not specified.

Location of Course

Location: 
London
United Kingdom

COURSE OVERVIEW

Our EViews Summer School will appeal to both new and experienced users of EViews and will provide attendees with a valuable insight in completing empirical work using the latest EViews software. All courses will teach econometrics from an applied perspective and demonstrate techniques using EViews software.

 

COURSE AGENDA

COURSE 1: EVIEWS BASICS

Session 1: Introduction

  • The notions of “workfile” and “object” in EViews
  • Data handling and databases in EViews

Session 2: Introduction II

  • Brief introduction to programming and series transformations in EViews
  • Data description: creating, editing, freezing and exporting graphs
  • Descriptive statistics and tests.

Session 3: The CLRM I

  • Preliminary theory for univariate regression: the classical linear regression model (CLRM), the CLRM assumptions, OLS estimation and regression statistics

Session 4: The CLRM II

  • Misspecification analysis: theory revision and diagnostic tests in EViews, stability tests and solutions to the misspecification problems
  • The General-to-Specific (GETS) approach

 

COURSE 2: ATHEORETICAL MODELS IN EVIEWS

Session 1: Atheoretical Models I

  • Statistical analysis of time series: definition of ARMA models, Box-Jenkins identification, trends and seasonality, filters
  • Stationarity and non stationarity: theory revision, the notion of unit roots, testing for unit roots in EViews, differencing series

Session 2: Univariate Forecasting I

  • Forecasting with ARMA models and measuring forecasting ability

Session 3: Atheoretical models II: Stationary VARs

  • VAR representation and estimation
  • Further testing with multivariate regression: Granger causality, lag selection

Session 4: Atheoretical Models III: Stationary VARs

  • Forecasting with VARs

 

COURSE 3: NON-STATIONARY TIME SERIES ANALYSIS IN EVIEWS

Session 1: Non-Stationarity I: Unit Roots

  • Introduction to the notion stationarity and unit roots
  • The Dickey-Fuller test

Session 2: Non-Stationarity II: Cointegration

  • Introduction to the notion of cointegration: preliminary theory and Engle-Granger analysis using EViews

Session 3: Multivariate Cointegration I: The VECM

  • Cointegrated VARs in EViews: Johansen’s test for cointegration, the (vector) error correction model (VECM), estimating and interpreting a VECM in EViews

Session 4: Multivariate Cointegration II: The VECM

Practical session

 

COURSE 4: TOPICS IN EVIEWS I: VOLATILITY MODELS AND PANEL DATA

Session 1: (G)Arch models I

  • Preliminary theory, representation and description of the main models

Session 2: (G)Arch models II

  • Estimation and regression output
  • Different specifications

Session 3: Panel Data I

  • Representation and estimation

Session 4: Panel Data II

Practical session

 

COURSE 5: TOPICS IN EVIEWS II

Session 1: Probit and Logit models I

  • Preliminary theory, representation and estimation

Session 2: Probit and Logit models II

  • Interpreting regression output
  • Forecasting

Session 3: Programming in EViews I

  • Preliminary notions

Session 4: Programming in EViews II

Practical session

More information

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