**Introduction to Time Series Using Stata**

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*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.

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.

1.1.2 Now some explanation

1.1.3 Navigating the interface

1.1.4 The gestalt of Stata

1.1.5 The parts of Stata speech

1.3 Looking at data

1.4 Statistics

1.4.2 Estimation

1.6 Making a date

1.6.2 Transformers

1.8 Looking ahead

2.2 Hypothesis tests

2.3 Linear regression

2.3.2 Instrumental variables

2.3.3 FGLS

2.5 Time series

2.5.2 ARMA models

What is the relationship between the data and the phenomenon of interest?

Who compiled the data?

What processes generated the data?

Are the data seasonally adjusted?

Are the data revised?

Cycle

Seasonal

3.3.2 Smoothing a cycle

3.3.3 Smoothing a seasonal pattern

3.3.4 Smoothing real data

3.4.2 EWMAs

dexponential: Double-exponential moving averages

shwinters: Holt–Winters smoothers including a seasonal component

4.1.2 Measuring the quality of a forecast

4.1.3 Elements of a forecast

4.2.2 Forecasting a trending series with a seasonal component

4.4 Looking ahead

5.2.2 Example: Mortgage rates (cont.)

The transformation strategy

The FGLS strategy

Comparison of estimates of model

5.4.3 Model 3: A lagged dependent variable with AR(1) errors

The IV strategy

5.6 Points to remember

6.2 Lag 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

7.2 The Box–Jenkins approach

7.3 Specifying an ARMA model

7.3.2 Step 2: Mind your p’s and q’s

7.5 Looking for trouble: Model diagnostic checking

7.5.2 Tests of the residuals

7.7 Comparing forecasts

7.8 Points to remember

7.9 What have we learned so far?

7.10 Looking ahead

8.2 ARCH: A model of time-varying volatility

8.3 Extensions to the ARCH model

8.3.2 Other extensions

Variations in volatility affect the mean of the observable series

Nonnormal errors

Odds and ends

9.2.2 Testing a VAR for stationarity

9.3.2 Summarizing temporal relationships in a VAR

How to impose order

FEVDs

Using Stata to calculate IRFs and FEVDs

9.4.2 Examples of a long-run SVAR

9.6 Looking ahead

10.2 Testing for unit roots

10.3 Cointegration: Looking for a long-term relationship

10.4 Cointegrating relationships and VECMs

10.4.1 Deterministic components in the VECM

10.5 From intuition to VECM: An example

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

10.7 Looking ahead

11.2 What did we miss?

11.2.2 Additional Stata time-series features

Univariate models

Multivariate models