The aim of this course is to provide participants with an in-depth understanding of the fundamental concepts behind time series modelling and forecasting with Stata. The course will provide the practical skills necessary to use Stata for modeling and forecasting of economic time series.
- Model and forecast from a univariate AR(FI)MA or multivariate VAR model.
- Model and forecast from a univariate GARCH (including EGARCH, TARCH, APARCH and GJR models) or a multivariate GARCH model ( including the CCC,DCC, VEC models).
- Distinguish between stationary and nonstationary series and understand the implications of using nonstationary series
- Build, estimate and forecast from univariate and multivariate time series models using Stata.
- Understand and critically evaluate recent research in time series.
This comprehensive webinar is hosted through Zoom and runs over a total of 9 hours, with 4 hours each day (2 in the morning and 2 in the afternoon) with an extra Q&A session on the second day.
||Q&A with Instructor
Two days of online instruction for four hours per day.
Two hours each morning, followed by two hours each afternoon.
Hour-long Q&A session at the end of each day to address queries.
- Getting started with time series data: visualization and testing.
- Unit roots tests, types of non –stationarity.
- Assessing the memory of economic and financial data over time: the auto covariance and autocorrelation functions.
- Pure time series models of the mean: AR, MA, ARMA, ARFIMA models: introduction, dependence structure and the Box Jenkins methodology to choose the best model.
- Estimation of AR(I)(FI)MA models in Stata.
- Post estimation diagnostic tests: is the model good enough? How to improve it.
- Forecasting with ARMA models.
- Adding exogenous variables to ARMA models: the ARDL framework.
- Pure time series models of the variance: the GARCH models.
- GARCH, EGARCH, TARCH, APARCH: estimation and post diagnostic tests in Stata.
- Do we really need a GARCH? The ARCH test.
- The ARMA-GARCH framework.
Capturing multivariate time dependences: the VAR and MGARCH models.
Estimation, testing, post diagnostic tests in Stata.
Which model is best for your research? Examples of successful research papers on time series analysis in Stata.
- Basic knowledge of linear regression is helpful but not necessary.
- An introductory level of Stata is not assumed.
- All costs exclude local taxes, where applicable.
- 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 enrollment).
- Additional discounts are available for multiple registrations.
- Cost includes course materials, lunch and refreshments.
- If you need assistance in locating hotel accommodation in the region, please notify us at the time of booking.
- Payment of course fees required prior to the course start date.
- Registration closes 5-calendar days prior to the start of the course.
- 100% fee returned for cancellations made over 28-calendar days prior to start of the course.
- 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
- No fee returned for cancellations made less than 14-calendar days prior to the start of the course.
The number of delegates is restricted. Please register early to guarantee your place.