Time Series Modelling and Forecasting Using Stata

The course begins with an introduction to Stata’s basic time series commands, before moving onto the analysis of time series features and to univariate time series models. Sessions 3 and 4 instead focus on the estimation of both multivariate time series models with stationary and nonstationary data and univariate models of volatility.

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 undertaken in Stata, during which the course tutor discusses and highlights potential pitfalls and the advantages of individual techniques. Particular attention is also given to both the interpretation and presentation of empirical results. In this manner, the course leader is able to bridge the “often difficult” gap between theory and practice of time series modelling and forecasting.

Upon completion, it is expected that participants are able to autonomously implement the statistical methods discussed during the course to their own data, customizing when necessary, the Stata do-file routines specifically developed for the course.

ONLINE FORMAT
The course has been developed to offer 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.

The 2023 edition of this training course will be offered ONLINE on a part-time basis on the 16th-17th and 23rd-24th of March.

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.

PREREQUISITE
Participants are required to have a good working knowledge of:

Linear regression model definition and assumptions.
Ordinary Least Squares (OLS) estimation. Properties of OLS.
Inference in the linear regression model: confidence intervals, t-test, F-test.
Violation of the linear regression model assumptions: heteroscedasticity, serial correlation, functional form misspecification, non-Normality. Consequences of violations and remedies.
Diagnostic analysis of regression: tests for heteroscedasticity, test for serial correlation, Normality test, Ramsey’s RESET.
Regression with time series data. Concepts of lagged variable and differenced variable.
Dynamic models.

Those needing to refresh these concepts are referred to:

Hill, R.C., Griffiths, W.E., and G.C. Lim (2018). Principles of Econometrics, 5th Edition. Wiley.
Stock, J.H., and Watson, M.W. (2019). Introduction to Econometrics, 4th edition, Pearson.
Wooldridge, J.M. (2020). Introductory Econometrics: A Modern Approach, 7th Edition, Cengage Learning.

PROGRAM
SESSION I: WORKING WITH TIME SERIES IN STATA
SESSION II: UNIVARIATE TIME SERIES MODELS
SESSION III: MULTIVARIATE TIME SERIES MODELS
SESSION IV: MODELLING AND FORECASTING VOLATILITY IN TIME SERIES MODELS

DATE AND LOCATION
The 2023 edition of this training course will be offered ONLINE on a part-time basis on the 16th-17th and 23rd-24th of March. To this end, programme includes a series of sessions based on 4 modules from 10:00 am to 1:30 pm Central European Time (CET).

FEES AND REGISTRATION
Students*: € 710.00
Ph.D Students: € 910.00
University: € 1010.00
Commercial: € 1345.00

*To be eligible for student prices, participants must provide proof of their full-time student status for the current academic year. 

Fees are subject to VAT (applied at the current Italian rate of 22%). Under current EU fiscal regulations, VAT will not however applied to companies, Institutions or Universities providing a valid tax registration number.

Course fees cover: course materials (handouts, Stata do files and datasets to be used during the course), a temporary licence of Stata valid for 30 days from the beginning of the course.

Individuals interested in attending the training course should contact TStat Training to ask for a registration form. The completed application must then be returned to TStat by the 6th March 2023.