Time Series Analysis in the Social Sciences The Fundamentals

377 Pages·2017·10.926 MB·English

by  Youseop Shin

Time Series Analysis in the Social
Sciences
Time Series Analysis in the
Social Sciences
THE FUNDAMENTALS
Youseop Shin
UNIVERSITY OF CALIFORNIA PRESS
University of California Press, one of the most distinguished
university presses in the United States, enriches lives around
the world by advancing scholarship in the humanities, social
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contributions from individuals and institutions. For more
information, visit www.ucpress.edu.
University of California Press
Oakland, California
© 2017 by Youseop Shin
Library of Congress Cataloging-in-Publication Data
Names: Shin, Youseop, 1964- author.
Title: Time series analysis in the social sciences : the
fundamentals / Youseop Shin.
Description: Oakland, California : University of
California Press, [2017] | Includes bibliographical
references and index.
Identifiers: LCCN 2016022158 (print) | LCCN
2016023292 (ebook) | ISBN 9780520293168 (cloth :
alk. paper) | ISBN 9780520293175 (pbk. : alk. paper)
| ISBN 9780520966383 (ebook)
Subjects: LCSH: Time-series analysis. | Social sciences
—Statistical methods.
Classification: LCC QA280 .S55 2017 (print) | LCC QA280
(ebook) | DDC 519.5/5—dc23
LC record available at https://lccn.loc.gov/2016022158
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10  9  8  7  6  5  4  3  2  1
To my parents, Dongjoon and Hangil,
my wife, Jungwon,
and my son, Lucky Boy, Jaeho
CONTENTS
Preface
1 Time Series Analysis in the Social Sciences
2 Modeling
(1) Preliminary Definition
(2) Preparing for Analysis
(3) Seasonal Components and Trend
(4) Systematic Patterns of Residuals
(5) Fitting the Residuals
(6) Further Reading
3 Diagnostics
(1) Residual Assumptions
(2) The Case of Monthly Violent Crime Rates, 1983–1992
(3) Further Reading
4 Forecasting
(1) How to Forecast Values
(2) Measuring the Accuracy of Time Series Models
(3) The Case of Monthly Violent Crime Rates, 1983–1992
(4) Further Reading
5 Smoothing
(1) Moving Average Smoothing
(2) Exponential Smoothing
(3) The Case of Monthly Violent Crime Rates, 1983–1992
(4) Further Reading
6 Time Series Analysis with Two or More Time Series
(1) Correlation and Regression Analysis
(2) Prewhitening
(3) Multiple Time Series Analysis with Lagged Variables
(4) Diagnostics
(5) Further Reading
7 Time Series Analysis as an Impact Analysis Method
(1) Interrupted Time Series Analysis
(2) The Case of Monthly Violent Crime Rates, 1985–2004
(3) Further Reading
Appendices
1. Links to Online Time Series Analysis Program
Manuals
2. U.S. Monthly Violent Crime Rates, 1983–2004
3. Data Resources for Social Science Time Series
Analysis
4. Statistical Tables
Notes
References
Index
PREFACE
In the social sciences, it generally takes longer to collect
temporal data than to collect cross-sectional data. In
addition, it is generally hard to obtain observations for as
many time points as are needed for good time series
analysis. For these reasons, time series analysis is employed
less frequently than cross-sectional analysis. Time series
analysis, however, can serve special purposes.
For example, we may identify and explain a systematic
temporal pattern of a variable, even when the variable does
not appear to change significantly over time. We can explain
and predict the dependent variable from the observations of
its past behavior, instead of relying on a limited set of
independent variables. Thereby, any independent variables
that may influence the dependent variable are taken into
account, and we can avoid threats to external validity of our
explanations of the dependent variable. In multiple time
series analysis, we can reflect any excluded important
independent variables in a lagged dependent variable on the
right-hand side of the equation. We can estimate how long an
estimated effect of an independent variable on the dependent
variable persists. By comparing two trends before and after
a program is implemented, we can test whether the program
brings about the expected effect.
To employ an appropriate time series analysis when
necessary, social scientists and professionals, such as
policymakers, campaign strategists, securities analysts, and
realtors, should first understand what time series analysis is,
and what we can do with it, and how. There are many books
on time series analysis. However, there are no books that
carefully and gently explain time series analysis for social
scientists and professionals. Most books on time series
analysis contain sophisticated mathematical materials, such
as matrixes and integral calculus, which most social
scientists and professionals do not have to understand for
their research and practice.
The purpose of this book is to provide practical, easy-to-
understand guidelines for time series analysis that are
suitable for social scientists and professionals. I intend to
make this book a primer for social scientists and
professionals which helps them learn and utilize time series
analysis without having to understand extraordinary
mathematical materials, which they are not likely to use
when they employ time series analysis as part of their
research and practice. Knowledge of regression analysis,
that is, how to estimate a slope and what are the properties
of residuals, can be helpful in understanding this book.