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Sessions
Dates Location Tuition Register
Nov 19 - 23, 2018 Rueil 3,310 €
Online By email
Who should attend?

Audience

    • Engineers, economists and financiers from all sectors.

Level

  • Proficiency
  • 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.
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.
Course Content

STATISTIC BASIS

0.5 day

  • 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

1.5 days

  • 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

1 day

  • 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

1 day

  • 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

1 day

  • ARCH model (AutoRegressive Conditional Heteroscedastic) and generalization.
  • Application: modeling volatility changes in the short term dynamic and on the equilibrium of Oil & Gas markets.
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Ways & Means
  • Applications performed on computer (statistical tests, development of econometric models, forecasting, simulation, highlighting cointegration and causality relationship, etc.) using Excel and Eviews.