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.