Introduction to Time Series Using Stata (Paperback)

Just enough Stata
Getting started
All about data
Looking at data
Statistics
Odds and ends
Making a date
Typing dates and date variables
Looking ahead

Just enough statistics
Random variables and their moments
Hypothesis tests
Linear regression
Multiple-equation modelsTime series

Filtering time-series data
Preparing to analyze a time series
Questions for all types of data
The four components of a time series
Some simple filters
Additional filters
Points to remember

A first pass at forecasting
Forecast fundamentals
Filters that forecast
Points to remember
Looking ahead

Autocorrelated disturbances
Autocorrelation
Regression models with autocorrelated disturbances
Testing for autocorrelation
Estimation with first-order autocorrelated data
Estimating the mortgage rate equation
Points to remember

Univariate time-series models
The general linear process
Lag polynomials: Notation or prestidigitation?
The ARMA model
Stationarity and invertibility
What can ARMA models do?
Points to remember
Looking ahead

Modeling a real-world time series
Getting ready to model a time series
The Box?Jenkins approach
Specifying an ARMA model
Estimation
Looking for trouble: Model diagnostic checking
Forecasting with ARIMA models
Comparing forecasts
Points to remember
What have we learned so far?
Looking ahead

Time-varying volatility
Examples of time-varying volatility
ARCH: A model of time-varying volatility
Extensions to the ARCH model
Points to remember

Models of multiple time series
Vector autoregressions
A VAR of the U.S. macroeconomy
Who’s on first?
SVARs
Points to remember
Looking ahead

Models of nonstationary time series
Trends and unit roots
Testing for unit roots
Cointegration: Looking for a long-term relationship
Cointegrating relationships and VECMs
Deterministic components in the VECM
From intuition to VECM: An example
Points to remember
Looking ahead

Closing observations
Making sense of it all
What did we miss?
Farewell