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Econometrics & Forecasting

5 days ENE/ECF
Level
Proficiency
Audience
  • Engineers, economists and financiers from all sectors.
Purpose
  • 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.
Prerequisite
  • Basic knowledge in the areas of statistics and Excel software.
Ways and means
  • Applications performed on computer (statistical tests, development of econometric models, forecasting, simulation, highlighting cointegration and causality relationship, etc.) using Excel and Eviews.

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.
2017 course calendar
Language Dates Location Tuition Register
Oct 09 - 13 Rueil €3,260 Online By email
Nov 20 - 24 Rueil €3,260 Online