Introduction to Time Series Using Stata, Revised Edition provides a step-by-step guide to essential time-series techniques–from the incredibly simple to the quite complex– and, at the same time, demonstrates how these techniques can be applied in the Stata statistical package. The emphasis is on an understanding of the intuition underlying theoretical innovations and an ability to apply them. Real-world examples illustrate the application of each concept as it is introduced, and care is taken to highlight the pitfalls, as well as the power, of each new tool. The Revised Edition has been updated for Stata 16.

Table of Contents:
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