Diagnostic Methods in Time Series

Fumiya Akashi is an Assistant Professor in the Faculty of Economics at the University of Tokyo. His research interests include time series analysis, robust inference for infinite variance processes and empirical likelihood method for time series models. Masanobu Taniguchi is a Professor in the Research Institute for Science and Engineering at Waseda University. His research interests include time series analysis, mathematical statistics, multivariate analysis, information geometry, signal processing, econometric theory and financial engineering. His main contributions in time series analysis are collected in his book “Asymptotic Theory of Statistical Inference for Time Series” (New York: Springer-Verlag, 2000). He received the Ogawa Prize (Japan) in 1989, the Econometric Theory Award (USA) in 2000, the Japan Statistical Society Prize in 2004, and Analysis Award in 2013 (Mathematical Society of Japan). He is a Fellow of the Institute of Mathematical Statistics (USA, 1987–). Anna Clara Monti is a Professor in the Department of Law, Economics, Management and Quantitative Methods at University of Sannio. Her research interests concern Statistical Inference, Robustness, Ordinal Response Models and Time Series. Tomoyuki Amano is an Associate Professor in Division of General Education at The University of Electro-Communications. He received the doctorate degrees in science from Waseda University, Japan and is now an associate professor in the Division of General Education at University of Electro-Communications, Japan. He is interested in research on financial time series and estimating function estimator for time series.

• Chapter 1. Elements of Stochastic Processes.- Chapter 2. Systematic approach for portmanteau tests in view of Whittle likelihood ratio.- Chapter 3. A new look at portmanteau test.- Chapter 4. Adjustments for a class of tests under nonstandard conditions.- Chapter 5. Adjustments for variance component tests in ANOVA models.- Chapter 6. Robust causality test of infinite variance processes.