2012 Econometrics Spring School

Timberlake Consultants are pleased to invite you to the 2012 Econometrics Spring School taking place at The George Washington University, Washington DC, USA. The Spring School comprises of three 2.5-day courses delivered by leading econometricians including Dr. Jurgen A. Doornik, Prof. Grayham Mizon, Dr. Sebastien Laurent and Dr. Jennifer Castle. The courses will run simultaneously on 12-14 March 2012, therefore attendees can only participate in one of the three courses on offer. This is a great opportunity for students, academics and professionals to expand their econometrics skills and keep up-to-date with major recent developments in applied econometric modelling.


Course 1: Econometric Modeling

Delivered By: Dr. Jurgen A. Doornik and Dr. Jennifer Castle
The course will cover the theory and practice of econometric modeling in a non-stationary and evolving world, when the model and mechanism differ. The following topics will be described in the course: how to embed theory models in selection; impulse-indicator saturation for handling multiple breaks during selection; simultaneous systems and VAR modeling; and tests for, and modeling of, non-linearity, super exogeneity and invariance.

Course 2: Economic Forecasting

Delivered By: Dr. Jennifer Castle and Prof. Grayham Mizon
The course will cover the theory and practice of economic forecasting facing a non-stationary and evolving world, when the model differs from the data generation process. A generalized taxonomy of forecast errors is developed, allowing for structural change in the forecast period, the model to be mis-specified over the sample period, and selected from sample evidence, the parameters of the model to be estimated (possibly inconsistently) from the data, which might be measured with error, the forecasts to commence from incorrect initial conditions, and innovation errors to cumulate over the forecast horizon. The taxonomy reveals the central role of unanticipated location shifts, and helps explain the outcomes of forecasting competitions. Other potential sources of forecast failure seem less relevant. Regime-shift non-stationarity can be removed by co-breaking (the cancellation of breaks across linear combinations of variables).

Course 3: Modelling Volatility

Delivered By: Dr. Sebastien Laurent
The course will cover the theory and practice of volatility modelling and forecasting. Traditional regression tools have shown their limitation in the modelling of financial time-series. Assuming that only the conditional mean could be changing with covariates while the variance remains constant over time often revealed to be an unrealistic assumption in practice. The following topics will be described in the course: the ARCH model and some of its most important extensions, multivariate GARCH models, value-at-risk forecasting, ranking volatility models in terms of their forecasting power, introduction of continuous-time stochastic volatility models and non-parametric estimators of the volatility, how to disentangle jumps and the smooth part of volatility, how to forecast volatility in presence of jumps, how to identify jumps.

Summary

Return to results