Introduction to Time Series Using Stata, by Sean Becketti, provides a practical guide to working with time-series data using Stata and will appeal to a broad range of users. The many examples, concise explanations that focus on intuition, and useful tips based on the author’s decades of experience using time-series methods make the book insightful not just for academic users but also for practitioners in industry and government.
The book is appropriate both for new Stata users and for experienced users who are new to time-series analysis.
Chapter 1 provides a mild yet fast-paced introduction to Stata, highlighting all the features a user needs to know to get started using Stata for time-series analysis. Chapter 2 is a quick refresher on regression and hypothesis testing, and it defines key concepts such as white noise, autocorrelation, and lag operators.
Chapter 3 begins the discussion of time series, using moving-average and Holt–Winters techniques to smooth and forecast the data. Becketti also introduces the concepts of trends, cyclicality, and seasonality and shows how they can be extracted from a series. Chapter 4 focuses on using these methods for forecasting and illustrates how the assumptions regarding trends and cycles underlying the various moving-average and Holt–Winters techniques affect the forecasts produced. Although these techniques are sometimes neglected in other time-series books, they are easy to implement, can be applied to many series quickly, often produce forecasts just as good as more complicated techniques, and as Becketti emphasizes, have the distinct advantage of being easily explained to colleagues and policy makers without backgrounds in statistics.
Chapters 5 through 8 encompass single-equation time-series models. Chapter 5 focuses on regression analysis in the presence of autocorrelated disturbances and details various approaches that can be used when all the regressors are strictly exogenous but the errors are autocorrelated, when the set of regressors includes a lagged dependent variable and independent errors, and when the set of regressors includes a lagged dependent variable and autocorrelated errors. Chapter 6 describes the ARIMA model and Box–Jenkins methodology, and chapter 7 applies those techniques to develop an ARIMA-based model of U.S. GDP. Chapter 7 in particular will appeal to practitioners because it goes step by step through a real-world example: here is my series, now how do I fit an ARIMA model to it? Chapter 8 is a self-contained summary of ARCH/GARCH modeling.
In the final portion of the book, Becketti discusses multiple-equation models, particularly VARs and VECs. Chapter 9 focuses on VAR models and illustrates all key concepts, including model specification, Granger causality, impulse-response analyses, and forecasting, using a simple model of the U.S. economy; structural VAR models are illustrated by imposing a Taylor rule on interest rates. Chapter 10 presents nonstationary time-series analysis. After describing nonstationarity and unit-root tests, Becketti masterfully navigates the reader through the often-confusing task of specifying a VEC model, using an example based on construction wages in Washington, DC, and surrounding states. Chapter 11 concludes.
Sean Becketti is a financial industry veteran with three decades of experience in academics, government, and private industry. He was a developer of Stata in its infancy, and he was Editor of the Stata Technical Bulletin, the precursor to the Stata Journal, between 1993 and 1996. He has been a regular Stata user since its inception, and he wrote many of the first time-series commands in Stata.
Introduction to Time Series Using Stata, by Sean Becketti, is a first-rate, example-based guide to time-series analysis and forecasting using Stata. It can serve as both a reference for practitioners and a supplemental textbook for students in applied statistics courses.
List of tables
List of figures
Preface
Acknowledgments
1. JUST ENOUGH STATA
Getting started
Action first, explanation later
Now some explanation
Navigating the interface
The gestalt of Stata
The parts of Stata speech
All about data
Looking at data
Statistics
Basics
Estimation
Odds and ends
Making a date
How to look good
Transformers
Typing dates and date variables
Looking ahead
2. JUST ENOUGH STATISTICS
Random variables and their moments
Hypothesis tests
Linear regression
Ordinary least squares
Instrumental variables
FGLS
Multiple-equation models
Time series
White noise, autocorrelation, and stationarity
ARMA models
3. FILTERING TIME-SERIES DATA
Preparing to analyze a time series
Questions for all types of data
How are the variables defined?
What is the relationship between the data and the phenomenon of interest?
Who compiled the data?
What processes generated the data?
Questions specifically for time-series data
What is the frequency of measurement?
Are the data seasonally adjusted?
Are the data revised?
The four components of a time series
Trend
Cycle
Seasonal
Some simple filters
Smoothing a trend
Smoothing a cycle
Smoothing a seasonal pattern
Smoothing real data
Additional filters
ma: Weighted moving averages
EWMAs
exponential: EWMAs
dexponential: Double-exponential moving averages
Holt–Winters smoothers
hwinters: Holt–Winters smoothers without a seasonal component
shwinters: Holt–Winters smoothers including a seasonal component
Points to remember
4. A FIRST PASS AT FORECASTING
Forecast fundamentals
Types of forecasts
Measuring the quality of a forecast
Elements of a forecast
Filters that forecast
Forecasts based on EWMAs
Forecasting a trending series with a seasonal component
Points to remember
Looking ahead
5. AUTOCORRELATED DISTURBANCES
Autocorrelation
Example: Mortgage rates
Regression models with autocorrelated disturbances
First-order autocorrelation
Example: Mortgage rates (cont.)
Testing for autocorrelation
Other tests
Estimation with first-order autocorrelated data
Model 1: Strictly exogenous regressors and autocorrelated disturbances
The OLS strategy
The transformation strategy
The FGLS strategy
Comparison of estimates of model
Model 2: A lagged dependent variable and i.i.d. errors
Model 3: A lagged dependent variable with AR(1) errors
The transformation strategy
The IV strategy
Estimating the mortgage rate equation
Points to remember
6. 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
7. MODELING A REAL-WORLD TIME SERIES
Getting ready to model a time series
The Box–Jenkins approach
Specifying an ARMA model
Step 1: Induce stationarity (ARMA becomes ARIMA)
Step 2: Mind your p’s and q’s
Estimation
Looking for trouble: Model diagnostic checking
Overfitting
Tests of the residuals
Forecasting with ARIMA models
Comparing forecasts
Points to remember
What have we learned so far?
Looking ahead
8. TIME-VARYING VOLATILITY
Examples of time-varying volatility
ARCH: A model of time-varying volatility
Extensions to the ARCH model
GARCH: Limiting the order of the model
Other extensions
Asymmetric responses to “news”
Variations in volatility affect the mean of the observable series
Nonnormal errors
Odds and ends
Points to remember
9. MODELS OF MULTIPLE TIME SERIES
Vector autoregressions
Three types of VARs
A VAR of the U.S. macroeconomy
Using Stata to estimate a reduced-form VAR
Testing a VAR for stationarity
Other tests
Forecasting
Evaluating a VAR forecast
Who’s on first?
Cross correlations
Summarizing temporal relationships in a VAR
Granger causality
How to impose order
FEVDs
Using Stata to calculate IRFs and FEVDs
SVARs
Examples of a short-run SVAR
Examples of a long-run SVAR
Points to remember
Looking ahead
10. 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
Step 1: Confirm the unit root
Step 2: Identify the number of lags
Step 3: Identify the number of cointegrating relationships
Step 4: Fit a VECM
Step 5: Test for stability and white-noise residuals
Step 6: Review the model implications for reasonableness
Points to remember
Looking ahead
11. CLOSING OBSERVATIONS
Making sense of it all
What did we miss?
Advanced time-series topics
Additional Stata time-series features
Data management tools and utilities
Univariate models
Multivariate models
Farewell
