Time series data are nowadays collected for several phenomena in social and empirical sciences. Initially collected at year or quarter level, time series data are now used by marketing analytics, financial technology, and other field in which data are collected at a much smaller interval (daily, hourly, and even by the minutes). This course focuses on the fundamental concepts required for the analysis, modelling and forecasting of time series data. The course provides an introduction to the theoretical foundation of time series models and a practical guide to the use of time series analysis techniques implemented in Stata 15. The course is based on the textbook by S. Boffelli and G. Urga (2016), Financial Econometrics Using Stata, Stata Press Publication.
The course will cover the following:
Day 1: Univariate Time Series Model
Sessions 1 & 2
- Stochastic processes and time series. Stationarity, autocorrelation, normality
- Univariate time series models: Moving Average (MA), Autoregressive (AR), Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA) models. The Box&Jenkins approach.
- Forecasting with ARMA models.
- Empirical application: Analysis of the features of time series. The Box&Jenkins approach in practice.
Sessions 3 & 4
- Unit root nonstationarity and main unit root tests: Augmented Dickey Fuller (ADF) and Phillips-Perron tests.
- Equilibrium (error) correction model.
- Spurious regression versus cointegration
- The Engle&Granger two-step procedure for modelling cointegrating relationships
- Empirical application: Estimating dynamic models and error correction models for nonstationary economic data.
Day 2: Multivariate Time Series Models
Sessions 5 & 6
- Stationary Vector Autoregression (VAR) modelling.
- Structural vector autoregression (SVAR).
- Granger causality.
- Impulse response function analysis.
- Empirical Application 2: Modelling the relationship between economic and financial stationary variables.
Sessions 7 & 8
- Non-stationary and cointegrated VARs
- The Johansen’s approach to multivariate cointegration.
- Empirical application 2: Modelling long-run relationships in economics and finance.
Arrival and Registration
Terms and 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.
- 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-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.