Introduction to Time Series Using Stata: 1st Edition (Paperback) book cover

Introduction to Time Series Using Stata

1st Edition

By Sean Becketti

Stata Press

741 pages | 111 B/W Illus.

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Paperback: 9781597181327
pub: 2013-01-02
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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.

Table of Contents

Just enough Stata

Getting started

All about data

Looking at data


Odds and ends

Making a date

Typing dates and date variables

Looking ahead

Just enough statistics

Random variables and their moments

Hypothesis tests

Linear regression

Multiple-equation modelsTime series

Filtering time-series data

Preparing to analyze a time series

Questions for all types of data

The four components of a time series

Some simple filters

Additional filters

Points to remember

A first pass at forecasting

Forecast fundamentals

Filters that forecast

Points to remember

Looking ahead

Autocorrelated disturbances


Regression models with autocorrelated disturbances

Testing for autocorrelation

Estimation with first-order autocorrelated data

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


Looking for trouble: Model diagnostic checking

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

Points to remember

Models of multiple time series

Vector autoregressions

A VAR of the U.S. macroeconomy

Who’s on first?


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

Points to remember

Looking ahead

Closing observations

Making sense of it all

What did we miss?


Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General