Web based: 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: $1295 Click here to register!
15% discount for group enrollments of three or more participants.
- 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
- 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.
|Startdato||12. mai 2020, 17:00|
|Sluttdato||15. mai 2020, 18:00|
|Siste frist||11. mai 2020, 0:00|