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The prospects are clear: we will probably live longer. The number
of people aged 65 and up will increase enormously over the next few
decades. Society will change as a result, but in what manner?
Europe and, in fact, probably the world faces the challenge of
preventing loneliness and isolation amongst a growing group of
senior people. The oldest part of the population is at particular
risk of becoming isolated and lonely as they grow older and their
work-related networks erode. While working in the field of
technology and aging, the authors discovered that there is a whole
new field to be explored, namely the phenomenon of
connectedness.This book is written by a group of authors with very
different backgrounds, varying from business, ICT, marketing,
anthropology, medicine, design and computer interaction. They all
felt the urge to explore this field of connectedness and they
discovered new opportunities for the emerging market of
aging-driven design . By unfolding the very nature of relationships
and age-based transitions in life, the authors invite the reader to
join them in an effort to design for connectedness: to reframe the
picture, rethink our options and reinvent how to connect!
With a new author team contributing decades of practical
experience, this fully updated and thoroughly classroom-tested
second edition textbook prepares students and practitioners to
create effective forecasting models and master the techniques of
time series analysis. Taking a practical and example-driven
approach, this textbook summarises the most critical decisions,
techniques and steps involved in creating forecasting models for
business and economics. Students are led through the process with
an entirely new set of carefully developed theoretical and
practical exercises. Chapters examine the key features of economic
time series, univariate time series analysis, trends, seasonality,
aberrant observations, conditional heteroskedasticity and ARCH
models, non-linearity and multivariate time series, making this a
complete practical guide. A companion website with downloadable
datasets, exercises and lecture slides rounds out the full learning
package.
With a new author team contributing decades of practical
experience, this fully updated and thoroughly classroom-tested
second edition textbook prepares students and practitioners to
create effective forecasting models and master the techniques of
time series analysis. Taking a practical and example-driven
approach, this textbook summarises the most critical decisions,
techniques and steps involved in creating forecasting models for
business and economics. Students are led through the process with
an entirely new set of carefully developed theoretical and
practical exercises. Chapters examine the key features of economic
time series, univariate time series analysis, trends, seasonality,
aberrant observations, conditional heteroskedasticity and ARCH
models, non-linearity and multivariate time series, making this a
complete practical guide. A companion website with downloadable
datasets, exercises and lecture slides rounds out the full learning
package.
Although many of the models commonly used in empirical finance are
linear, the nature of financial data suggests that non-linear
models are more appropriate for forecasting and accurately
describing returns and volatility. The enormous number of
non-linear time series models appropriate for modeling and
forecasting economic time series models makes choosing the best
model for a particular application daunting. This classroom-tested
advanced undergraduate and graduate textbook, first published in
2000, provides a rigorous treatment of recently developed
non-linear models, including regime-switching and artificial neural
networks. The focus is on the potential applicability for
describing and forecasting financial asset returns and their
associated volatility. The models are analysed in detail and are
not treated as 'black boxes'. Illustrated using a wide range of
financial data, drawn from sources including the financial markets
of Tokyo, London and Frankfurt.
This is the most up-to-date and accessible guide to one of the fastest growing areas in financial analysis by two of the most accomplished young econometricians in Europe. This classroom-tested advanced undergraduate and graduate textbook provides an in-depth treatment of recently developed nonlinear models, including regime-switching and artificial neural networks, and applies them to describing and forecasting financial asset returns and volatility. It uses a wide range of financial data, drawn from sources including the markets of Tokyo, London and Frankfurt.
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