Introduction to Time Series Using Stata, Revised Edition, by Sean Becketti, is a practical guide to working with time-series data using Stata. In this book, Becketti introduces time-series techniques—from simple to complex—and explains how to implement them using Stata. The many worked examples, concise explanations that focus on intuition, and useful tips based on the author’s experience make the book insightful for students, academic researchers, and practitioners in industry and government.
Becketti is a financial industry veteran with decades of experience in academics, government, and private industry. He was also a developer of Stata in its infancy and has been a regular Stata user since its inception. He wrote many of the first time-series commands in Stata. With his abundant knowledge of Stata and extensive experience with real-world time-series applications, Becketti provides advice and examples that bring each chapter to life.
For those new to Stata, the book begins with a mild yet fast-paced introduction to Stata, highlighting all the features you need to know to get started using Stata for time-series analysis. Before diving into analysis of time series, Becketti includes a quick refresher on statistical foundations such as regression and hypothesis testing.
The discussion of time-series analysis begins with techniques for smoothing time series. As the moving-average and Holt–Winters techniques are introduced, Becketti explains the concepts of trends, cyclicality, and seasonality and shows how they can be extracted from a series. The book then illustrates how to use these methods for forecasting. Although these techniques are sometimes neglected in other time-series books, they are easy to implement, can be applied 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.
Next, the book focuses on single-equation time-series models. Becketti discusses regression analysis in the presence of autocorrelated disturbances as well as the ARIMA model and Box–Jenkins methodology. An entire chapter is devoted to applying these techniques to develop an ARIMA-based model of U.S. GDP; this will appeal to practitioners, in particular, 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? The discussion of single-equation models concludes with a self-contained summary of ARCH/GARCH modeling.
In the final portion of the book, Becketti discusses multiple-equation models. He introduces VAR models and uses a simple model of the U.S. economy to illustrate all key concepts, including model specification, Granger causality, impulse–response analyses, and forecasting. Attention then turns to nonstationary time-series. 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.
Introduction to Time Series Using Stata, Revised Edition, by Sean Becketti, is a first-rate, example-based guide to time-series analysis and forecasting using Stata. Researchers and students learning to analyze time-series data and those wanting to implement time-series methods in Stata will want a copy of this book at hand.
List of tables
List of figures
Preface
Acknowledgments
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
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
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
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
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
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
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
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
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
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
