1st Edition

Introduction to Time Series Using Stata, Revised Edition

ISBN 9781597183062
Published March 2, 2020 by Stata Press
446 Pages 111 B/W Illustrations

USD $79.95

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Book Description

Introduction to Time Series Using Stata, Revised Edition provides a step-by-step guide to essential time-series 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. The Revised Edition has been updated for Stata 16.

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 models
Time series

Filtering time-series data
Preparing to analyze a time series
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 prestidigitations?
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

Model 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 times series
Trend and unit roots
Testing for unit roots
Cointegration: Looking for a long-term relationship
Cointegrating relationships and VECM
From intuition to VECM: An example
Points to remember
Looking ahead

Closing observations
Making sense of it all
What did we miss?


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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.