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Econometrics & Forecasting

Econometrics & Forecasting

5 days
ENE/ECF
Sessions
This course is not scheduled.
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
  • to perform an econometric estimation
  • to 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 AND 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 AND 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 AND 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 and 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 and gas 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