COURSE OVERVIEW

 

Time Series data is today available for a wide range of several phenomena in Business, Finance, Economics, Public Health, the Political and Social Sciences. The aim of our Times Series Modelling and Forecasting Course is therefore to provide researchers and professionals with the standard tool kit required for the analysis of time series data in Stata. As such the programm has been developed to offers an overview of the most commonly used methods for analysing, modelling and forecasting the dynamic behaviour of time series data, offering practical examples of empirical modelling using real-world data. Module 1 provides an introduction to Stata’s basic commands before moving to the analysis of time series features and to univariate time series models. Module 2 covers multivariate time series models for stationary and non-stationary series.

 

TARGET AUDIENCE

 

Researchers and professionals working in financial institutions, policy institutions, research departments of utilities, governments, corporations, Ph.D and Master students in biostatistics, economics, finance, engineering, psychology, social and political sciences needing to implement time series data analysis methods.

 

COURSE REQUISITES

 

Participants should have a knowledge of the inferential statistics and introductory econometric methods illustrated in Wooldridge, J. M (2019). Participants are not required to be familiar with the statistical software Stata.


PROGRAM

 

SESSION I: WORKING WITH TIME SERIES IN STATA

 

A quick introduction to Stata for time series data:

 

Importing datasets
Creating and formatting date variables using date and time functions and declaring datasets to be time-series
Using time-series operators to create lags
Differences
Leads

 

Graphical analysis of time series:

 

Line plot
Correlogram
Histogram

 

Testing for autocorrelation and testing for unit root

 

Univariate time series models: theoretical elements and practical applications of modelling real-world macroeconomic time series with the arima command

 

Modelling volatility: univariate ARCH/GARCH models. Theoretical elements and practical applications of modelling real-world financial time series with the arch command

 

Forecasting with AR(I)MA-ARCH models

 

 

 

 

SESSIONS II: MULTIVARIATE TIME SERIES MODELS

 

Stationary Vector Autoregression (VAR) modelling: theoretical elements and practical applications of modelling real-world macroeconomic time series with the var command

 

Checking correct specifi cation of VAR models: diagnostic tests and plots

 

Granger causality and impulse response function analysis

 

Non-stationary time series: an introduction to cointegration

 

Vector error-correction models: theoretical elements and practical applications of modelling real-world macroeconomic time series with the vecm command

 

SUGGESTED READING (PRE – AND POST-COURSE)

 

Introduction to Time Series Using Stata. Stata Press Publication, S. Becketti (2020).

 

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