Training Calendar

Stata School in Time Series

Stata School in Time Series

COURSE DATE: 5-7 February 2018

Time series data is nowadays collected for several phenomena in social and empirical sciences. This school 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 Stata15.

The course is based on the textbook Financial Econometrics Using Stata by S.Boffelli and G.Urga (2016), Stata Press

Day 1 

Session 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 appraoch
  • Forecasting with ARMA models
  • Empirical applucation 1: Analysis of the features of time series. The Box & Jenkins approach in practice

Session 3 & 4: 

  • Multivariate time series models: Vector Autoregressions (VAR)
  • Structural vector autoregression (SVAR)
  • Granger causality 
  • Impulse response function analysis 
  • Empirical Application 2: Modelling the relationship between economic and financial stationary variables

Day 2

Session 1 & 2: 

  • Unit root nonstationarity and main unit root tests: Augmented Dickey Fuller (ADF) and Phillips-Perron tests
  • Autoregressive distributed lag model (ADL)
  • Equilibrium (error) correction model 
  • Empirical application 1: Estimating dynamic models and error correction models for nonstationary economic data

Session 3 & 4:

  • Spurious regression versus cointegration
  • The Engle & Granger two-step procedure for modelling cointegrating relationships
  • The Johansen's approach to multivariate cointegration
  • Empirical application 2: Modelling long-run relationships in economics and finance 

 Day 3

Session 1 & 2: 

  • Volatility: features and measures
  • Univariate models of conditional volatility: ARCH, GARCH, GARCH-in-mean, and IGARCH models
  • Asymmetric GARCH models (SAARCH, EGARCH, GJR, TGARCH, APARCH). Leverage effect and news impact curve. 
  • Empirical Application 1: Modelling asset returns volatility via alternative univariate GARCH models. 

Session 3 & 4: 

  • Multivariate models of conditional volatility (MGARCH): Diagonal VECH model, Constant Conditional Correlation (CCC), Dynamic Conditional Correlation model (DCC)
  • Model diagnostic
  • Forecasting with unicariate and multivariate GARCH models
  • Empirical Application 2: Modelling conditional correlations between asset returns with alternative multicariate GARCH models

 Day 4

Session 1 & 2

  • How to use big data efficiently 
  • Principal component analysis 
  • Static and dynamic factor models 
  • Empirical Application 1: Identifying global, asset related and country specific factors in data with a large number of assets

Session 3 & 4

  • Value-at-risk (VaR) to measure market risk
  • Parametric model, historical simulation, Monte Carlo simulation
  • Backtesting procedures: unilevel VaR tests
  • Empirical Application 2: Value at Risk estimation in commodity markets 

Learning Ratio: 

40% Theory, 30% Demonstration and 30% Practical 

Daily Timetable 

TimeSession / Description
09:00-09:20 Arrival & Registration
09:30-11:00 Session 1
11:00-11:15 Tea/coffee break
11:15-12:45 Session 2
12:45-13:45 Lunch
13:45-15:15 Session 3
15:15-15:30 Tea/coffee break
15:30-17:00 Session 4



Basic knowledge of statistics and econometrics and STATA is required. 

Financial Econometrics Using Stata - Stata Press Publication - S.Boffelli and G.Urga (2016)

Introduction to Time Series Using Stata - Stata Press Publication - S. Becketti (2013)


Several academic papers will be suggested during the course to complement 




  •  CommercialAcademicStudent
    1 Day pass (05/02/2018 - 07/02/2018)
    2 Day pass (05/02/2018 - 07/02/2018)
    3 Day pass (05/02/2018 - 07/02/2018)

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Cass Business School is located approximately five to ten minutes walk from the nearest underground and railway stations (Moorgate, Old Street, Barbican and Liverpool Street).

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