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Time-series analysis using Stata


This course reviews methods for time-series analysis and shows how to perform the analysis using Stata. The course covers methods for data management, estimation, model selection, hypothesis testing, and interpretation. For univariate problems, the course covers autoregressive moving-average (ARMA) models, linear filters, long-memory models, unobserved components models, and generalized autoregressive conditionally heteroskedastic (GARCH) models. For multivariate problems, the course covers vector autoregressive (VAR) models, cointegrating VAR models, state-space models, dynamic-factor models, and multivariate GARCH models. Exercises will supplement the lectures and Stata examples.

Price: $1,395  

We offer a 15% discount for group enrollments of three or more participants.

Course leader

David Schenck portrait

David Schenck is a Senior Econometrician at StataCorp LLC. He earned his bachelor's degree in economics from Vanderbilt University and a PhD in economics from Boston College. His interests include time series, Bayesian analysis, and macroeconomics. At Stata, he is the primary developer of DSGE and other time-series features.

Course topics

  • A quick review of the basic elements of time-series analysis
  • Managing and summarizing time-series data
  • Univariate models
    • Moving average and autoregressive processes
    • ARMA models
    • Stationary ARMA models for nonstationary data
    • Multiplicative seasonal models
    • Deterministic versus stochastic trends
    • Autoregressive conditionally heteroskedastic models
    • Autoregressive fractionally integrated moving average model
    • Tests for structural breaks
    • Markov switching models
  • Introduction to forecasting in Stata
  • Filters
    • Linear filters
    • A quick introduction to the frequency domain
  • The univariate unobserved components model
  • Multivariate models
    • Vector autoregressive models
    • A model for cointegrating variables
  • State-space models
  • Impulse response and variance decomposition analysis
  • Dynamic-factor models
  • Multivariate GARCH


A general familiarity with Stata and a graduate-level course in regression analysis or comparable experience.

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Enrollment is limited. This course is offered in both classroom and web-based settings.

Classroom training courses are two-day courses that run from 8:30 a.m. to 4:30 p.m. each day. These courses take place at a training center where computers with Stata installed are provided. A continental breakfast, lunch, and an afternoon snack will also be provided; the breakfast is available before the course begins.

Web-based training courses are four-day courses that run for three and a half hours each day. You will be provided with a temporary Stata license to install on your computer, a printed copy of the course notes, and all the course datasets so that you can easily follow along. Learn more about how our web-based training courses work, watch a video demonstration, and find technical requirements for participating in this type of training.