A comprehensive and timely edition on an emerging new trend in time
series
Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and
GARCH sets a strong foundation, in terms of distribution theory, for
the linear model (regression and ANOVA), univariate time series analysis
(ARMAX and GARCH), and some multivariate models associated primarily
with modeling financial asset returns (copula-based structures and the
discrete mixed normal and Laplace). It builds on the author's previous
book, Fundamental Statistical Inference: A Computational Approach,
which introduced the major concepts of statistical inference. Attention
is explicitly paid to application and numeric computation, with examples
of Matlab code throughout. The code offers a framework for discussion
and illustration of numerics, and shows the mapping from theory to
computation.
The topic of time series analysis is on firm footing, with numerous
textbooks and research journals dedicated to it. With respect to the
subject/technology, many chapters in Linear Models and Time-Series
Analysis cover firmly entrenched topics (regression and ARMA). Several
others are dedicated to very modern methods, as used in empirical
finance, asset pricing, risk management, and portfolio optimization, in
order to address the severe change in performance of many pension funds,
and changes in how fund managers work.
- Covers traditional time series analysis with new guidelines
- Provides access to cutting edge topics that are at the forefront of
financial econometrics and industry
- Includes latest developments and topics such as financial returns
data, notably also in a multivariate context
- Written by a leading expert in time series analysis
- Extensively classroom tested
- Includes a tutorial on SAS
- Supplemented with a companion website containing numerous Matlab
programs
- Solutions to most exercises are provided in the book
Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and
GARCH is suitable for advanced masters students in statistics and
quantitative finance, as well as doctoral students in economics and
finance. It is also useful for quantitative financial practitioners in
large financial institutions and smaller finance outlets.
