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2018 EViews Summer School, London

  • Location: City University, London
  • Duration: 5 days (4th June 2018 - 8th June 2018)
  • Software: EViews
  • Level: Various
  • Delivered By: Prof. Lorenzo Trapani
  • Topic: Econometrics, Forecasting, Statistics, Summer School, Various methods
2018 EViews Summer School, London

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.

The Summer School comprises the following courses:

All courses will teach econometrics from an applied perspective and demonstrate techniques using EViews software.

Course Agenda

Course 1: EViews Basics

Date: Monday, 4 June 2018
Level: Introductory
Learning ratio: 90% Practical; 10% Theory

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: Time Series Modelling in EViews

Date: Tuesday, 5 June 2018
Level: Intermediate
Learning ratio: 50% Practical; 50% Theory

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: Panel Data Models in EViews

Date: Wednesday, 6 June 2018
Level: Intermediate
Learning ratio: 50% Practical; 50% Theory

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: Volatility Modelling and Forecasting

Date: Thursday, 7 June 2018
Level: Intermediate
Learning ratio: 50% Practical; 50% Theory

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: Discrete Choice Models

Date: Friday, 8 June 2018
Level: Intermediate / Advanced
Learning ratio: 50% Practical; 50% Theory

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

Pre-course Reading List

Course 1: EViews Basics

  • EViews Help Files

Course 2: Time Series Modelling in EViews

  • Brooks, C., (2002). Introductory Econometrics for Finance, Cambridge University Press.

Course 3: Panel Data Models in EViews

  • Brooks, C., (2002). Introductory Econometrics for Finance, Cambridge University Press.
  • Hamilton, J.D., (1994). Time Series Analysis, Princeton University Press.

Course 4: Volatility Modelling and Forecasting

  • Baltagi, B.H., (2008). Econometric Analysis of Panel Data, Wiley Press.

Course 5: Discrete Choice Models

  • Probit/logit: Greene, W., (2003). Econometric Analysis, Prentice Hall.
  • Programming: EViews Help Files

Post-course Reading List

Course 1: EViews Basics

  • EViews Help Files.

Course 2: Time Series Modelling in EViews

  • Hamilton, J.D., (1994). Time Series Analysis, Princeton University Press.

Course 3: Panel Data Models in EViews

  • Hamilton, J.D., (1994). Time Series Analysis, Princeton University Press.

Course 4: Volatility Modelling and Forecasting

  • To be discussed during the course

Course 5: Discrete Choice Models 

  • To be discussed during the course

Prerequisites

Course 1: EViews Basics

  • No prior knowledge of EViews required
  • Basic Regression and Statistics knowledge

Course 2: Time Series Modelling in EViews

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

Course 3: Panel Data Models in EViews

  • Some prior knowledge of EViews
  • Knowledge of OLS models, Diagnostic tests, ARMA models and Stationarity

Course 4: Volatility Modelling and Forecasting 

  • Some prior knowledge of EViews
  • Knowledge of Panel Data and GARCH models

Course 5: Discrete Choice Models 

  • Prior knowledge of EViews and its programming language
  • Basic knowledge of of Probit / Logit models

Terms and 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, lunch and refreshments.
  • 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. (Alternatively, laptops can be hired for a fee of £10.00 (ex. VAT) per day).
  • If you need assistance in locating hotel accommodation in the region, please notify us at the time of booking.
  • Payment of course fees required prior to the course start date.
  • Registration closes 5-calendar days prior to the start of the course.
    • 100% fee returned for cancellations made over 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 delegates is restricted. Please register early to guarantee your place.

  •  CommercialAcademicStudent
    1-day pass (04/06/2018 - 04/06/2018)
    2-day pass (04/06/2018 - 05/06/2018)
    3-day pass (04/06/2018 - 06/06/2018)
    4-day pass (04/06/2018 - 07/06/2018)
    5-day pass (04/06/2018 - 08/06/2018)

All prices exclude VAT or local taxes where applicable.

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