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On-site training

Time-series analysis using Stata

Description

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.

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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
• 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

Prerequisite

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

Notes

This is a two-day course. All training courses generally run for eight hours per day and include morning and afternoon breaks and a lunch break. You can arrange a convenient schedule with your instructor and training coordinator.

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