Presented By: Dr. Malvina Marchese (Cass Business School, City, University of London)
This course offers a comprehensive discussion of advanced time series models in EViews and their applications to a wide range of fields including: financial econometrics and macro econometrics. The course offers both a theoretical discussion of the models and many practical applications to time series data.
Strong emphasis is placed on interpretation of the results and identifying the best performing model from a forecasting perspective.
We consider several advanced time series models available in EViews 12: Markov switching models, Regime switching threshold Models, Star Models, Midas models and their comparisons.
10am-12pm (London time)
2pm-4pm (London time)
4pm-4:30pm (London time)
Day 1: the MIDAS touch
Traditional approaches to time-series estimation and forecasting in economics require the variables to be at the same frequency. This often causes a problem since most macroeconomic data is reported at different intervals and frequencies. Mixed-Data Sampling (MIDAS) is a method of estimating and forecasting using models where the dependent variable is recorded at a lower frequency than one or more of the independent variables. Unlike the traditional aggregation approach, MIDAS uses information from every observation in the higher frequency space.
Creating Mixed frequencies workfiles
Almon/PDL lag weighted MIDAS regression (estimation, interpretation , post estimation diagnostic checks)
Step weighted MIDAS regression (estimation, interpretation, post estimation diagnostic checks)
Beta weighted MIDAS regression (estimation, interpretation, post estimation diagnostic checks)
Machine learning methods to select the MIDAS weight function (new in EViews 12)
Forecasting with MIDAS
Day 2 : Regime switching models
Misspecification in time series models: what can go wrong?
Detecting structural breaks in time series with EViews
The TAR model: estimation and forecasting with an observable threshold
The SETAR model: estimation and forecasting with a self -exciting threshold
The Star model: estimation and forecasting with smooth regime transaction
Markov switching models: estimation and forecasting
And the winner is? Forecasting comparison of advanced time series models: an in depth discussion with the most recent methods developed in the econometric literature
Basic knowledge of hypothesis testing is needed.
Basic knowledge of EViews is helpful.
Basic understanding of regression analysis is helpful.
Terms & Conditions
Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrolment).
Additional discounts are available for multiple registrations.
Cost includes course materials that will be posted to you prior to the start of the course.
Delegates are provided with temporary licences for the software(s) used in the course and will be instructed to download and install the software prior to the start of the course.
Payment of course fees required prior to the course start date.
Registration closes 1-day prior to the start of the course.