Econometrics & ForecastingGER/ECF

Who should attend?

  • To be able to use econometric tools in order to determine correlations and adjustments between physical or economical series and to make forecasts and simulations.
Audience :
  • Engineers, economists and financiers from all sectors.

Level : Proficiency

Course Content

  • STATISTIC BASIS

      • Descriptive statistics (mean, median, standard-deviation, etc.), indices (Laspeyres, Paasche, Divisia).
      • Statistical tests (normality, student, Fisher).
      • Application: energy data set (quantities, prices).
  • LINEAR REGRESSION MODELS & FORECASTING

      • Simple and multiple linear regression models, ordinary least square estimator, R2.
      • Application: energy demand model.
      • Statistical tests validating econometric models: autocorrelation (Durbin-Watson, Lagrangian multiplier), heteroscedasticity (White and Breusch-Pagan), multicollinearity (BKW).
      • Structural change on linear regression model (Chow test, Brown-Durbin & Evans test).
      • Application: analysis of the substitution between oil, gas and electricity.
      • Principle of forecasting with an econometric model (properties of the estimator, prediction interval).
      • Application: forecasts on energy demand model.
  • TIME SERIES ANALYSIS & FORECASTING

      • Time series model.
      • Smoothing techniques for short run forecasts: extrapolation techniques (moving average, time series decomposition with trend and seasonal pattern).
      • Application: monthly energy demand series (with a seasonal pattern), forecast over 12 month.
      • ARIMA models (AutoRegressive Integrated Moving Average), tests assessing the stochastic processes (number of autoregressive and moving average lags, stationnarity).
      • Application: ARIMA model simulations.
  • TIME SERIES RELATIONSHIP: COINTEGRATION & CAUSALITY

      • Introduction to cointegration techniques: unit root tests (Dickey-Fuller, Phillips-Perron, KPSS), Engle and Granger model, long term equilibrium, Error Correction Model (ECM).
      • Causality test.
      • Application: cointegration techniques to Oil & Gas markets.
      • Cointegration with multiple relationship: Johanson test (max. eigenvalue and Trace test) on a VAR (Vectorial AutoRegressive) model.
      • Application: modeling the equilibrium between prices over several market places.
      • Structural changes on cointegration model: long term and short term dynamic (Perron test, Gregory and Hansen test).
  • CHANGES OF VOLATILITY ON ENERGY MARKET

      • ARCH model (AutoRegressive Conditional Heteroscedastic) and generalization.
      • Application: modeling volatility changes in the short term dynamic and on the equilibrium of Oil & Gas markets.

Learning Objectives

  • Upon completion of the course, the participants will be able to:
  • use the main econometric techniques,
  • perform an econometric estimation,
  • develop models and make forecasts, in particular in the energy sector and on financial markets.

Ways & Means

  • Applications performed on computer (statistical tests, development of econometric models, forecasting, simulation, highlighting cointegration and causality relationship, etc.) using Excel and Eviews.