Introduction to Time Series Using Stata, Revised Edition

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 models Time series Filtering time-series data Preparing to analyze a time series 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 prestidigitations? 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 Model 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 times series Trend and unit roots Testing for unit roots Cointegration: Looking for a long-term relationship Cointegrating relationships and VECM From intuition to VECM: An example Points to remember Looking ahead Closing observations Making sense of it all What did we miss? Farewell References