Recent decades have witnessed explosive growth in new and powerful tools for timeseries analysis. These innovations have overturned older approaches to forecasting, macroeconomic policy analysis, the study of productivity and long-run economic growth, and the trading of financial assets. Familiarity with these new tools on time series is an essential skill for statisticians, econometricians, and applied researchers.

Introduction to Time Series Using Stata provides a step-by-step guide to essential timeseries 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.

Sean Becketti is a financial industry veteran with three decades of experience in academics, government, and private industry. Over the last two decades, Becketti has led proprietary research teams at several leading financial firms, responsible for the models underlying the valuation, hedging, and relative value analysis of some of the largest fixed-income portfolios in the world.

1. Just enough Stata 1.1 Getting started 1.2 All about data 1.3 Looking at data 1.4 Statistics 1.5 Odds and ends 1.6 Making a date 1.7 Typing 1.8 Looking ahead 2. Just enough statistics 2.1 Random variables and their moments 2.2 Hypothesis tests 2.3 Linear regression 2.4 Multiple-equation models 2.5 Time series 3. Filtering time-series data 3.1 Preparing to analyze a time series 3.2 The four components of a time series 3.3 Some simple filters 3.4 Additional filters 3.5 Points to remember 4. A first pass at forecasting 4.1 forecast fundamentals 4.2 Filters that forecast 4.3 Points to remember 4.4 Looking ahead 5. Autocorrelated disturbances 5.1 Autocorrelation 5.2 Regression models with autocorrelated disturbances 5.3 Testing for autocorrelation 5.4 Estimation with first-order autocorrelated data 5.5 Estimation the mortgage rate equation 5.6 Points to remember 6. Univariate time-series models 6.1 The general linear process 6.2 Log polynomials: Notation or prestidigitation? 6.3 The ARMA model 6.4 Stationarity and invertibility 6.5 What can ARMA models do? 6.6 Points to remember 6.7 Looking ahead 7. Modeling a real-world time series 7.1 Getting ready to dodel a time series 7.2 The Box-Jenkins approach 7.3 Specifying an ARMA model 7.4 Estimation 7.5 Looking for trouble: Model diagnostic checking 7.6 forecastion with ARIMA models 7.7 comparing forecasts 7.8 Points to remember 7.9 What have we learned so far? 7.10 Looking ahead 8. Time-varying volatility 8.1 Examples of time-varying volatility 8.2 ARCH: A model of time-varying volatility 8.3 Extensions to the ARCH model 8.4 Points to remember 9 Models of multiple time series 9.1 Vector autoregressions 9.2 A VAR of the U.S. macroeconomy 9.3 Who's on first? 9.4 SVARs 9.5 Points to remember 9.6 Looking ahead 10. Models of nonstationary time series 10.1 Trends and unit roots 10.2 Testing for unit roots 10.3 Cointegration:Looking for a long-term relationship 10.4 Cointegrating relationships and VECMs 10.5 Freom intuition to VECM: An example 10.6 Points to remember 10.7 Looking ahead 11. Closing observations 11.1 Making sense of it all 11.2 What did we miss? 11.3 Farewell