Course content Lecture 1: Introduction
- Time-series data in Stata
- Working with dates
- Time-series operators
- Drawing graphs
- Simple smoothers and forecasting techniques
- Moving averages
- Exponential smoothers
- Holt–Winters forecasting
- The nature of time series
- Autocorrelation
- White noise
- Stationarity
- Time-series processes
- Moving average (MA)
- Autoregressive (AR)
- Mixed autoregressive moving average (ARMA)
- The sample autocorrelation and partial autocorrelation functions
- Introduction to spectral analysis—the periodogram
- Basic ARIMA models
- Using ARMA processes to model series
- Choosing the number of AR and MA terms
- Selecting the best model from information criteria
- Forecasting
- Seasonal ARIMA models
- Models with exogenous regressors—ARMAX models
- A brief tour of intervention analysis
- Additive outliers
- Level shifts
- Autocorrelation
- Testing for autocorrelation
- Obtaining Newey–West standard errors
- More on ARMAX models
- Seasonal effects
- Nonstationarity and unit-root tests
- Heteroskedasticity in time series
- Autoregressive conditional heteroskedasticity (ARCH) models
- Generalized ARCH (GARCH) models and extensions
- Testing for ARCH effects
Bonus lecture: Overview of multivariate time-series analysis using Stata
- Vector autoregressive (VAR) models
- Estimating VAR models
- Impulse–response analysis
- Forecasting
- Structural VARs
- Cointegration
- Testing for cointegration
- Vector error-correction (VEC) models
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