Distribution Theory for Linear and Time-Series Models aims to set 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 title, Fundamental Statistical Inference: A Computational Approach, which introduces the major concepts of statistical inference.
Attention is explicitly paid to application and numeric computation, with examples of Matlab code throughout. The point of including code is to offer a framework for discussion and illustration of numerics, and to show the mapping from theory to computation.
The book also includes:
- A tutorial on SAS
- A companion website containing various programs and a solutions manual for students
- Includes latest developments and topics such as financial returns data, notably also in a multivariate context.
This book 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 (investment division, risk management division, and model validation division) to smaller finance outlets.
The goal of this book project is to set a strong foundation, in terms of (usually small-sample)
distribution theory, for the linear model (regression and ANOVA), univariate time series
analysis (ARMAX and GARCH), and some multivariate models associated primarily with
modeling nancial asset returns (copula-based structures and the discrete mixed normal and
Laplace). It builds on the author's Fundamental Statistical Inference: A Computational
Approach, which introduces the major concepts, and some extra material, on statistical
inference in the i.i.d. case. I hereafter denoted it as book III (and likewise referring to
my books on probability theory as books I and II). The target audiences are advanced
masters students in statistics and quantitative nance, though should also serve well for
less quantitatively oriented doctoral students in economics and nance who wish (or need)
to take a course in econometrics.
