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.
A general familiarity with Stata and a graduate-level course in regression analysis or comparable experience.
This course is available in-person or virtually. In-person training courses generally run for eight hours per day and include morning and afternoon breaks and a lunch break. Virtual training courses are typically divided into three- to four-hour daily sessions. You can arrange a convenient schedule with your instructor.