# Introduction to Time Series Using Stata

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

Looking at data
Statistics

Basics
Estimation

Odds and ends
Making a date

How to look good
Transformers

Typing dates and date variables

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

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

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

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?

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

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

11. CLOSING OBSERVATIONS

Making sense of it all
What did we miss?

Additional Stata time-series features

Data management tools and utilities
Univariate models
Multivariate models

Farewell Author: Sean Becketti
Edition: Revised Edition
ISBN-13: 978-1-59718-306-2