Training Calendar

Summer School Course - EViews Econometrics

Online 6 days (8th June 2020 - 13th June 2020) EViews Intermediate, Introductory, Various
Econometrics, Forecasting, Statistics, Summer School, Various methods

Overview

The 2020 EViews Summer School comprises a series of six 1-day courses running consecutively, with theory and practical applications. All courses will teach econometrics from an applied perspective and demonstrate techniques using EViews 11 software.

The summer school will run for 6 consecutive days for four hours per day in two sessions per day: morning 10-12 GMT and afternoon 2-4pm GMT. The morning sessions will discuss theory and the afternoon sessions will offer many practical applications in EViews

Course Agenda

The summer school will run for 6 consecutive days for four hours per day in two sessions per day: morning 10-12 GMT and afternoon 2-4pm GMT. The morning sessions will discuss theory and the afternoon sessions will offer many practical applications in EViews

Day 1: Introduction to EViews basics. The classical linear regression model:

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: CLRM I:

    • Preliminary theory for univariate regression: the Classical Linear Regression Model (CLRM), the CLRM assumptions, OLS estimation and regression statistics.
    • Session 4: 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.
      • Day 2: Atheoretical models in EViews:

        ARMA models (identification and forecasting); Vector AutoRegressions (identification and forecasting).

        Atheoretical Models:

        • 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.

        Univariate Forecasting:

        • Forecasting with ARMA models and measuring forecasting ability.
        • The properties of short and long run forecasts from ARMA models

        Multivariate Atheoretical Models: Stationary VARs:

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

        Multivariate Forecasting: Stationary VARs:

        • Forecasting with VARs.
        • Measuring forecasting ability: indicators and the Diebold-Mariano test; other tests
        • Day 3: Models for nonstationary series:

          Non-stationarity I: Unit Roots:

          • Introduction to the notion stationarity and unit roots.
          • The Dickey-Fuller test.
          • Non-stationarity II: Cointegration:

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

            Multivariate Cointegration: The VECM:

          • Cointegrated VARs in EViews: Johansen’s test for cointegration, the (Vector) Error Correction Model (VECM), estimating and interpreting a VECM in EViews.
          • Multivariate Forecasting with non stationary data:

          • Forecasting with the VECM.
          • Day 4: Non-linear models:

            Markov switching regressions and conditional volatility models.

            Markov switching models I:

          • Preliminary theory, representation and description of the main models.
          • Markov switching models II:

            • Estimation and regression output.
            • Different specifications.

            (G)Arch Models I:

          • Preliminary theory, representation and description of the main models.
          • (G)Arch Models II:

            • Estimation and regression output.
            • Different specifications.

            Day 5: State Space models:

            Representation (and Eviews symtax); Estimation, time-varying parameter models and models with latent variables.

            State space models I: Representation

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

            State space models II: Estimation

            • Estimation of a state space object
            • Setting initial values

            State space models III: time-varying parameter models and models with latent variables

            • Setting up and estimating a time-varying parameter model in EViews
            • Models with latent variables: the NAIRU

            State-space models IV: Forecasting

            • The “state space” object procedures
            • Forecasting states and signals
            • Kalman-Bucy filtering
            • Forecasting parameters
            • Day 6: Models

              Models for panel data and discrete choice in Eviews.

              Session 1: Panel Data I:

              • Representation and estimation.
                • A strategy to choose between fixed and random effects

                Session 2: Panel Data II:

                • Short dynamic panels: GMM estimation.
                  • Unit roots and cointegration.

                  Session 3: Probit and Logit Models I:

                  • Preliminary theory, representation and estimation.

                  Session 4: Probit and Logit Models II:

                  • Interpreting regression output.
                  • Forecasting.

Prerequisites

  • Knowledge of basic econometrics (OLS estimation, mis-specification analysis) is desirable, although not required.
  • No previous knowledge of EViews is required.

Principal texts for course reading

  • Course 1:
  • EViews Help Files.
  • Course 2:
  • Brooks, C., (2002). Introductory Econometrics for Finance, Cambridge University Press.
  • Course 3:
  • Brooks, C., (2002). Introductory Econometrics for Finance, Cambridge University Press.
  • Hamilton, J.D., (1994). Time Series Analysis, Princeton University Press.
  • Course 4:
  • Hamilton, J.D., (1994). Time Series Analysis, Princeton University Press.
  • Course 5:
  • Hamilton, J.D., (1994). Time Series Analysis, Princeton University Press.
  • Course 6:
  • Probit/logit: Greene, W., (2003). Econometric Analysis, Prentice Hall.
  • Baltagi, B.H., (2008). Econometric Analysis of Panel Data, Wiley.

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. Contact us for more information.
  • 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.
  • Cost includes course materials that will be posted to you prior to the start of the course.
  • Payment of course fees required prior to the course start date.
  • Registration closes 1-day prior to the start of the course.
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