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2018 EViews Forecasting Summer School, London

  • Location: Cass Business School, Bunhill Row, London EC1Y 8TZ
  • Duration: 5 days (30th July 2018 - 3rd August 2018)
  • Software: EViews
  • Level: Various
  • Delivered By: Dr. Lorenzo Trapani (Cass Business School)
  • Topic: Econometrics, Forecasting, Statistics, Summer School, Various methods
2018 EViews Forecasting Summer School, London

Course Overview

The 2018 EViews Forecasting Summer School, taking place at Cass Business School, London, UK comprises a series of five 1-day courses running consecutively between 30 Jul - 3 Aug 2018.

This is a great opportunity for students, academics and professionals to expand their forecasting skills and learn how they can apply a range of techniques.

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

Our 2018 EViews Forecasting Summer School will take place on 30 Jul - 3 Aug 2018 in London. The School comprises 5x 1-day courses providing complete flexibility to the participants to attend one, a combination of, or all five courses.

The Forecasting Summer School comprises the following courses:

  • Course 1: Course 1: Introduction to EViews
  • Course 2: Forecasting with EViews Part 1
  • Course 3: Forecasting with EViews Part 2
  • Course 4: Forecasting non-stationary series
  • Course 5: State Space Modelling in EViews

 

Our EViews Forecasting Summer School is a dedicated series of short courses aimed at the forecasting practitioner. The courses will appeal to both new and experienced users of EViews and will provide attendees with valuable insights on forecasting (and problems encountered with forecasting) completed empirically using EViews software.

Course Agenda

Course 1: Introduction to EViews

Level: Introductory
Learning ratio: 90% Practical; 10% Theory

The course introduces EViews most popular and useful commands and procedures to import, manipulate, transform and manage data, as well as to perform some commonly used statistical routines and econometric estimations. This session is ideal for new or beginner EViews users, and we strongly recommend any participant with no prior experience of using EViews and planning to take further courses, should attend this course.

Course 2: Forecasting with EViews Part 1

Level: Introductory Learning ratio: 70% Practical; 30% Theory

The basics of forecasting (and of using EViews) are introduced. The day contains a fair balance of theory and application, and both the ordinary regression model and ARMA models are introduced.

Session 1: Introduction

  • The notions of “workfile” and “object” in EViews
  • Data handling and databases in EViews
  • Data description: creating, editing, freezing and exporting graphs
  • Descriptive statistics and tests

Session 2: Univariate forecasting I: Structural models

  • Preliminary theory for univariate regression: the classical linear regression model (CLRM), the CLRM assumptions, OLS estimation
  • Creating, estimating and reading a regression in EViews

Session 3: Univariate forecasting II: Atheoretical models

  • ARMA models: Box-Jenkins identification, estimation and forecasting
  • Smoothing

Session 4: Measuring forecasting performance

  • Some indicators of forecasting accuracy: mean squared and mean absolute errors, Theil’s U
  • The Diebold-Mariano test

Course 3: Forecasting with EViews Part 2

Level: Intermediate
Learning ratio: 50% Practical; 50% Theory

The first half of the day (more theoretical) presents a more advance look at the ordinary regression model, focusing on the issue of misspecification – i.e., from the point of view of forecasting, the issue of improving a regression model (and/or an estimation technique). In the second half of the day (more practical), the notions of ARMA models and regression are extended to a multivariate context.

Session 1: Univariate forecasting III: Misspecification tests

  • More on the CLRM assumptions
  • Testing for misspecification: heteroskedasticity, normality, functional form

Session 2: Univariate forecasting IV: Dynamic models

  • Specification of dynamic models
  • Testing for structural instability

Session 3: Multivariate forecasting I: Stationary VARs

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

Session 4: Multivariate forecasting II: Forecasting stationary VARs

  • The notion of “model”, static and dynamic prediction
  • Indicators of forecasting accuracy

Course 4: Forecasting non-stationary series

Level: Intermediate
Learning ratio: 50% Practical; 50% Theory

The main focus here is on non-stationary data. After introducing the notions of stationarity and non-stationarity (and some tests), we discuss cointegration in both a single equation and multiple equation framework.

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 forecasting II: Vector error correction models

  • 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 forecasting II: Forecasting using the VECM

  • Creating and interpreting forecasts with non-stationary VARs
  • Static and dynamic prediction
  • Modelling and predicting UK inflation

Course 5: State space modelling in EViews

Level: Intermediate / Advanced
Learning ratio: 50% Practical; 50% Theory

The sessions are mainly practical in this case, and it could be worth revising the theory either before the session or after. A very good reading is Chapter 13 in: Hamilton, J.D., (1994). Time Series Analysis, Princeton University Press. The sessions will cover: setting up a State-space model in EViews, forecasting and getting around some typical problems (mainly, trying to achieve convergence). The main focus will be on a special case of a State-space model, namely the time-varying parameter case. It is natural to link this topic with the discussion on structural instability during Course 3.

Session 1: State space models I: Representation

  • Representation and creation of a state space object in EViews
  • Unobserved components

Session 2: State space models II: Estimation

  • Estimation of a state space object
  • Setting initial values

Session 3: State space models III: The time-varying parameter model

  • Setting up and estimating a time-varying parameter model in EViews

Session 4: State-space models IV: Forecasting

  • The “state space” object procedures
  • Forecasting states and signals
  • Kalman-Bucy filtering
  • Forecasting parameters

Pre-course reading list

  • Course 1: Introduction to EViews
    • EViews Help Files.
  • Course 2: Univariate forecasting with EViews
    • Brooks, C., (2002). Introductory Econometrics for Finance, Cambridge University Press.
  • Course 3: Multivariate forecasting with EViews
    • Brooks, C., (2002). Introductory Econometrics for Finance, Cambridge University Press.
  • Course 4: Forecasting non-stationary series
    • Brooks, C., (2002). Introductory Econometrics for Finance, Cambridge University Press.
    • Hamilton, J.D., (1994). Time Series Analysis, Princeton University Press.
  • Course 5: State Space Modelling in EViews
    • Hamilton, J.D., (1994). Time Series Analysis, Princeton University Press.

Post-course reading list

Course 1: Introduction to EViews

  • EViews Help Files.

Course 2: Univariate forecasting with EViews

  • To be discussed during the course.

Course 3: Multivariate forecasting with EViews

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

Course 4: Forecasting non-stationary series

  • Hamilton, J.D., (1994). Time Series Analysis, Princeton University Press.
  • Greene, W., (2003). Econometric Analysis, Prentice Hall.

Course 5: State Space Modelling in EViews

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

Prerequisites

Course 1: Introduction to EViews
  • No prior knowledge of EViews required.
  • Basic regression and statistics knowledge.
  • Course 2: Univariate forecasting with EViews
    • Some prior knowledge of EViews.
    • Basic regression and statistics knowledge.
  • Course 3: Multivariate forecasting with EViews
    • Some prior knowledge of EViews.
    • Knowledge of OLS models, ARMA models and some forecasting techniques.
  • Course 4: Forecasting non-stationary series
    • Some prior knowledge of EViews.
    • Knowledge of OLS models, diagnostic tests, ARMA models and stationarity.
  • Course 5: State Space Modelling in EViews
    • Some prior knowledge of EViews.
    • Knowledge of state-space modelling.
  • 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.
    • 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, we can also provide laptops for a small daily charge).
    • If you need assistance in locating hotel accommodation, 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.

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