Course Description
Course Programme
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Terms and Conditions
About OxMetrics

Timberlake Consultants Ltd, the distributor and publisher of the OxMetrics™ software, would like to invite you to attend a four-day course, in Central London.

This is an intensive course designed for researchers and advanced students (PhD).The objective is to get familiar with more advanced time-series methods and techniques for the analysis of time-series data using. Several modules of the OxMetrics software will be used throughout the course. Many hands-on sessions are part of the course.No experience with OxMetrics is required but a reasonable levelof statistics, econometrics, time series analysis and programmingis assumed.

Participants may want to combine this course with one or more of the following courses/events

The course - Several major advances in time series, forecasting and software engineering have occurred in the past years. These advances have provided a major breakthrough in the modelling of time series using easy-to-use object-oriented Windows-based software. This course aims to provide participants with a thorough understanding of the methods used to model time series. This includes ARMA, GARCH, ARFIMA, Maximum likelihood estimation, unobserved components, state space, the Kalman filter, stochastic volatility and signal extraction and forecasting. It further demonstrates how this innovative modelling and forecasting methodology can be implemented in real-life business, industry and government situations. Participants are invited to send their own data in Excel format prior to the start of the course.

Definitions

• OxMetrics is a modular system for the econometric and statistical analysis of economic, financial and marketing data. The following modules of OxMetrics will be used during the course
  • PcGive is the module that provides a graphical user interface for dynamic econometric models, optionally using Autometrics.
  • STAMP is the module that provides a graphical user interface for unobserved components time series modelling based on the Kalman filter.
  • Ox is the matrix and statistical programming language which is usedto compliment the graphical facilities.
  • SsfPack is a suite of C routines for carrying out computations involving the statistical analysis of univariate and multivariate models in state space form. It requires Ox to run.

Who should attend? - Structural time series models find application in many subjects, including, economics, finance, sociology, management science, biology, geography, meteorology, transportation, tourism and engineering. The course is, therefore, suitable for anyone who works with time series data in business, industry and government or for those who teach or research time series at universities. Only a basic knowledge in regression and time series analysis is assumed. Familiarity with the software is not required. If you want to get some familiarity with the software prior to the course, please request a demo copy.

The Principal Lecturers - The principal lecturers are:
Dr. Jurgen Doornik is the managing director of OxMetrics Technologies Ltd. and Research Fellow at Nuffield College, Oxford. He is the originator of the Ox Object-Oriented Programming language, and contributed to many Ox packages (including the ARFIMA package - now integral part of PcGive) and works with David Hendry on PcGive. He has published papers in the Econometrics Journal, Journal of Economic Surveys, and Scottish Journal of Political Economy.
Prof. Siem Jan Koopman is a Professor in Econometrics at the Vrije Universiteit Amsterdam. He gained his Ph.D. (from LSE) in 1992 and worked earlier at the LSE and Tilburg University . He has published articles in Biometrika, JASA, J Business and Economics Statistics, and J Royal Statistical Society - Series B. He is co-editor of Econometrics J and J of Forecasting and is editorial board member of J of Applied Econometrics. He is the main contributor to the OxMetrics™ module STAMP™ and developed the Ox package SsfPack.

http://www.doornik.com
http://stamp-software.com
http://www.ssfpack.com