COURSE LEADER: ELISABETTA PELLINI COURSE ID: D-EF39-OL LANGUAGE:

TIME SERIES MODELLING AND FORECASTING USING STATA

Time series data is nowadays collected for several phenomena in social and empirical sciences. The aim of this course is to show participants how to use Stata to perform analysis, modelling and forecasting of time series. The course provides an overview of methods for analysing, modelling and forecasting the dynamic behaviour of economic time series and offers several practical examples of empirical modelling using real-world data. The course does not require any previous knowledge of Stata, since 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.

In common with TStat’s training philosophy, throughout the course theory and methods are illustrated in an intuitive way and are complemented by practical exercises with Stata. In this manner, the course leader is able to bridge the “often difficult” gap between theory and practice of time series modelling and forecasting. At the end of the course, participants are expected to be able to autonomously implement the methods discussed in the course.

Researchers and professionals working in financial institutions, policy institutions, research departments of utilities, governments, corporations, Ph.D and Master students in economics, finance, engineering needing to learn the time series methods.

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.

SESSION I: WORKING WITH TIME SERIES IN STATA

A quick introduction to Stata for time series data:

Importing datasets

Creating and formatting data 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 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 AR(I)MA-ARCH models

SESSION 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 specification 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

COURSE REFERENCES 

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).

Due to the current Public Health situation in Europe, we unfortunately have to reschedule this course date. We will be monitoring the virus situation very carefully over the forthcoming weeks, so as to be in a position to publish a feasible updated course schedule as soon as possible. Please accept our apologies for any inconvenience caused.


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Time series data is nowadays collected for several phenomena in social and empirical sciences. The aim of this course is to show participants how to use Stata to perform analysis, modelling and forecasting of time series. The course provides an overview of methods for analysing, modelling and forecasting the dynamic behaviour of economic time series and offers several practical examples of empirical modelling using real-world data. The course does not require any previous knowledge of Stata, since 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.