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• Course outline
• Follow along
• What is so special about time-series analysis?
• Time-series data in Stata
• The basics
• Clocktime data
• Time-series operators
• The lag operator
• The difference operator
• The seasonal difference operator
• Combining time-series operators
• Working with time-series operators
• Parentheses in time-series expressions
• Percentage changes
• Drawing graphs
• Basic smoothing and forecasting techniques
• Four components of a time series
• Moving averages
• Exponential smoothing
• Holt–Winters forecasting

• The nature of time series
• Stationarity
• Autoregressive and moving-average processes
• Moving-average processes
• Autoregressive processes
• Stationarity of AR processes
• Invertibility of MA processes
• Mixed autoregressive moving-average processes
• The sample autocorrelation and partial autocorrelation functions
• A detour
• The sample autocorrelation function
• The sample partial autocorrelation function
• A brief introduction to spectral analysis—The periodogram

• Basic ideas
• Forecasting
• Two goodness-of-fit criteria
• More on choosing the number of AR and MA terms
• Seasonal ARIMA models
• Additive seasonality
• Multiplicative seasonality
• ARMAX models
• Intervention analysis and outliers
• Final remarks on ARIMA models

• Basic regression analysis
• Autocorrelation
• The Durbin–Watson test
• Other tests for autocorrelation
• Estimation with autocorrelated errors
• The Newey–West covariance matrix estimator
• ARMAX estimation
• Cochrane–Orcutt and Prais–Winsten methods
• Lagged dependent variables as regressors
• Dummy variables and additive seasonal effects
• Test for structural break
• Nonstationary series and OLS regression
  • Unit-root processes
• ARCH
• A simple ARCH model
• Testing for ARCH
• GARCH models
• Extensions
• Markov-switching models
• Markov-switching dynamic regression
• Markov-switching autoregression
• Threshold regression
• A self-exciting threshold model
• A second threshold model
• Letting threshold choose the number of regimes

Note: The previous four lessons constitute the core material of the course. The following lesson is optional and introduces Stata’s multivariate time-series capabilities.

• VARs
• The VAR(p) model
• Lag-order selection
• Diagnostics
• Granger causality
• Forecasting
• Impulse–response functions
• Orthogonalized IRFs
• VARX models
• VECMs
• A basic VECM
• Fitting a VECM in Stata
• Impulse–response analysis