Book review / International Journal of Forecasting 25 (2009) 209–211
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another branch of the dealership is offering the stately
saloon at the same price, but without the free insurance
(this is a called a decoy) the attraction of the saloon at
your branch of the dealers suddenly rockets and you
wonder why you ever considered the smaller car.
There are many other topics in this book that
will fascinate and surprise. Ariely demonstrates how
difficult it is for us to predict how we will behave when
our decision making is subject to extreme emotions
like anger or sexual desire. He discusses why our
decision making suffers so often from procrastination,
why we tend to overvalue what we already possess,
and how our expectations determine our subsequent
experiences. But this is not just a book that documents
our biases and inconsistencies in an entertaining way.
In every chapter there is a discussion of the wider
implications of our irrationality and how these might
be mitigated.
Although a comprehensive list of academic papers is
referenced in the bibliography, the book is suitable
for a general reader or anyone requiring a gentle
introduction to the psychology of decision making
or behavioral economics. For the forecaster who has
the task of predicting how people will behave in
given circumstances (for example, how they will
respond to a price increase, an advertising campaign
or the introduction of a new product) there is plenty
of valuable and thought provoking material here.
Behavioral economics is a relatively new subject, and
it is not without controversy, but in the long run it
might help forecasters to develop more reliable models
or enable people to make judgmental forecasts with
greater insight and understanding.
Introduction to Time Series Analysis and
Forecasting, Douglas C. Montgomery, Cheryl L.
Jennings, Murat Kulahci. Wiley, (2008). 446 pp.,
$115, ISBN: 978-0-471-65397-4
smoothing, autoregressive integrated moving average
models (ARIMA), transfer functions, and intervention
models. In the final chapter they also present
a survey of other forecasting methods, including
multivariate time series models, state space models,
direct forecasting of percentiles, aggregation and
disaggregation, and neural networks.
Chapter 1 introduces time series analysis and
forecasting and gives a good exposition of the main
concepts of forecasting and the role of forecasting
as a useful analytical tool. The much longer chapter
2 delves into the details of statistical methods as
applied to time series and forecasting methods. It also
discusses the concept of the monitoring of forecasting
models, which is not often covered thoroughly
in forecasting texts. The discussion includes the
Shewhart control charts, cumulative error tracking
signals, and smoothing error tracking signals. To
supplement the discussion, the authors provide clear
examples of these monitoring methods and how
analysts can use them to make effective decisions.
Analyzing time-oriented data and forecasting are
among the most important problems that analysts
face across many fields, ranging from finance and
economics to production operations and the natural
sciences. As a result, analysts from a variety of
disciplines need to understand the concepts of
time series analysis and forecasting. The goal of
Introduction to Time Series Analysis and Forecasting
is to help analysts to understand these methods and
make decisions based on the evidence. The authors
provide concrete examples which use output from
SAS, Minitab c and other software packages; in
addition, each chapter contains copious exercises that
allow readers to check their understanding of the
methods presented in each chapter. Included in the
topics in this book are regression analysis, exponential
0169-2070/$ - see front matter
doi:10.1016/j.ijforecast.2008.11.004
Paul Goodwin
The Management School,
University of Bath,
Bath, BA2 7AY, United Kingdom
E-mail address: mnspg@bath.ac.uk.
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Book review / International Journal of Forecasting 25 (2009) 209–211
Chapter 3 posits the concept of regression analysis,
since the heart of many of these time series and
forecasting methods is regression analysis using
calculus and matrix algebra. The calculus is used to
derive the OLS estimators used in regression, while
matrix algebra is used to compute them. The authors
make good use of Minitab c to illustrate the regression
output and to show how these estimates were derived.
The derivations will be useful, for this enhances the
comprehension of regression analysis. The authors
acknowledge that regression analysis for time series
data has some problems (e.g., autocorrelation). They
provide a generalized least squares (GLS) framework
as a remedy for the problem of autocorrelation in
time series. They also discuss discounted least squares,
unlike most other introductory books. In OLS, the
older observations receive the same weight as recent
observations. However, the recent observations may be
more important for the true behavior of the time series
process; therefore, the use of the discounted least
squares method allows older observations to receive
less weight than more recent observations. The final
part of the chapter presents regression methods as they
are applied to time series and forecasting methods.
This discussion covers the standard material on
autocorrelation and the Durbin–Watson test statistic,
as well as methods for correcting for autocorrelation,
such as Cochrane–Orcutt and maximum likelihood
estimators (MLE). This discussion is enhanced with
copious examples of SAS output, namely using the
procedure PROC AUTOREG.
Chapter 4 provides the standard discussion on exponential smoothing methods, with the first part presenting the moving average method, then shifting to
exponential smoothing methods. The final discussion
of exponential smoothing as applied to seasonality is
useful to analysts, since much of the work with time
series involves the issue of seasonality.
Chapter 5 introduces the autoregressive integrated
moving average (ARIMA) models, known as the Box–
Jenkins approach. The first part of the chapter provides
separate discussions of the moving average and
autoregressive processes, followed by PACF and ACF,
which are used extensively in ARIMA modeling. This
material is followed by a discussion of ARMA and
ARIMA models. A comprehensive discussion ensues
about how the analyst can use ACFs and PACFs in the
determination of the AR and MA processes, as well
as ARMA. The development of ARIMA models is
often not straightforward, and the authors present the
standard approach to ARIMA model building: model
identification, parameter estimation, and diagnostic
checking. This discussion is supplemented with actual
data, with output from Minitab c . These concrete
examples provide a greater understanding of how
these models are developed. The final section of the
chapter uses these ARIMA models in forecasting.
Chapter 6 introduces transfer function and intervention models. The authors make it clear that the development of the transfer functions is more arduous
than the development of the ARIMA models. Intervention analysis examines the impacts of a known event
(e.g., a change in the regulatory procedure or a catastrophic event), and represents a qualitative change in
state. Then the analyst would want to know the nature
and magnitudes of the impacts of these interventions
on the time series models and forecasts. The authors
discuss how to develop these intervention models and
how to interpret their results.
The final chapter is a smorgasbord of topics pertaining to time series and forecasting, including multivariate time series models: vector autoregressive
models, autoregressive conditional heteroscedasticity
(ARCH) and generalized autoregressive conditional
heteroscedasticity (GARCH) models, state space models, direct forecasting of percentiles, aggregation and
disaggregation, and neural networks. These are only
introduced, and someone wanting more comprehensive information about these time series and forecasting methods would need to read supplementary materials in order to learn the details of these methods.
Some of the early chapters are lengthy and include
details about methods, but these details are essential
if one wishes to develop a good, comprehensive
understanding of these analytical methods. The
discussion of ARIMA modeling in chapter 5 is
thorough, but the authors might have stressed that
the use of ARIMA in short-term forecasting is
appropriate, but that longer term forecasts tend to
be overstated. The final chapter is a brief overview
of other time series and forecasting methods, but a
reader might be better served if some of these methods
were covered in greater detail. A relevant topic for a
chapter might have been structural time series models,
which would have provided a nice complement to
the ARIMA models. Structural time series models
Book review / International Journal of Forecasting 25 (2009) 209–211
look at the unobserved components, such as trend,
seasonality, cyclical and irregular components, and the
analyst can develop forecasts based on these models.
Brian W. Sloboda was formerly an economist
at the Bureau of Transportation Statistics in the US
Department of Transportation, and at the Bureau
of Economic Analysis in the US Department of
Commerce. Currently, he is a pricing economist at the
US Postal Service. He is also teaching economics and
211
statistics for the University of Phoenix, University of
Maryland, Park University, and the USDA Graduate
School. He has written numerous articles on the topics
of regional economics, transportation economics, and
labor economics.
Brian Sloboda
8750 Georgia Ave, 112B, Silver Spring, MD, 20910,
United States
E-mail address: bsloboda@email.phoenix.edu.