Introduction to Time-Series Analysis using Stata - (2 days)

Time-series analysis is gaining popularity across many disciplines for its ability to detect trends. This two-day course takes a practical applied approach (rather than mathematical) and participants are provided with detailed, hands-on experience in running basic time-series analysis using Stata.

Master Class - runs over 2 days

Dr Joanna Dipnall is a biostatistician with the School of Public Health and Preventative Medicine (SPHPM) at Monash University and Honorary Research Fellow with School of Medicine at Deakin University. She holds a B.Ec(Honours) from Monash University, and a PhD from the School of Medicine at Deakin University. She also lectures and tutors with the Department of Statistics, Data Science and Epidemiology at Swinburne University. Joanna has developed a novel Risk Index for Depression (RID) utilising SEM and machine learning techniques that brought together five key determinants of depression. She has been a teacher of Stata software for over 15 years, training across Australia and overseas and was a member of the Scientific Committee for the Oceania Stata Users Group Meeting in 2017.

About this course: 

Time-series analysis is gaining popularity across many disciplines for its ability to detect trends. This course is an introduction to time-series concepts and univariate time-series analysis. Univariate time-series is a sequence of measurements of the same variable collected over time, often at regular intervals. The aim of a univariate time-series model is to best describe the pattern of the time-series, whereby researchers can explain things such as the impact of the past on the future, or even forecast future values to extrapolate beyond the range of the explanatory variables.


Stata is one of the most widely used statistical software packages for time-series analysis. This course aims to provide a foundation working knowledge of time-series analysis methods using Stata. The two-day course will provide an introduction to forecasting and explain time-series and its components. An introduction to smoothing a data series, moving average, exponential smoothing and forecasting.


The emphasis of this two-day course is on methods and analysis of time-series data using Stata statistical software.

Course syllabus: 

First Day

  • Introduction to Time-series: What is it and how is it used
  • Preparing time-series data in Stata
  • The four components in a time-series: Trend, cycle, seasonality, and irregular movements
  • Weighted moving averages
  • Exponential smoothing


Second Day

  • Forecasting fundamentals: types and elements
  • Trend models
  • Autocorrelation and models
  • Univariate time-series models: ARMA and ARIMA models
  • Forecasting with ARIMA models
Recommended Texts: 

Participants will be provided with detailed tailored notes for this course.

Supported by: 

Stata is distributed in Australia and New Zealand by Survey Design and Analysis Services.