Diagnostic Methods in Time Series

Β· Springer Nature
αžŸαŸ€αžœαž—αŸ…β€‹αž’αŸαž‘αž·αž…αžαŸ’αžšαžΌαž“αž·αž…
108
αž‘αŸ†αž–αŸαžš
αž€αžΆαžšαžœαžΆαž™αžαž˜αŸ’αž›αŸƒ αž“αž·αž„αž˜αžαž·αžœαžΆαž™αžαž˜αŸ’αž›αŸƒαž˜αž·αž“αžαŸ’αžšαžΌαžœαž”αžΆαž“αž•αŸ’αž‘αŸ€αž„αž•αŸ’αž‘αžΆαžαŸ‹αž‘αŸ αžŸαŸ’αžœαŸ‚αž„αž™αž›αŸ‹αž”αž“αŸ’αžαŸ‚αž˜

αž’αŸ†αž–αžΈαžŸαŸ€αžœαž—αŸ…β€‹αž’αŸαž‘αž·αž…αžαŸ’αžšαžΌαž“αž·αž€αž“αŸαŸ‡

This book contains new aspects of model diagnostics in time series analysis, including variable selection problems and higher-order asymptotics of tests. This is the first book to cover systematic approaches and widely applicable results for nonstandard models including infinite variance processes. The book begins by introducing a unified view of a portmanteau-type test based on a likelihood ratio test, useful to test general parametric hypotheses inherent in statistical models. The conditions for the limit distribution of portmanteau-type tests to be asymptotically pivotal are given under general settings, and very clear implications for the relationships between the parameter of interest and the nuisance parameter are elucidated in terms of Fisher-information matrices. A robust testing procedure against heavy-tailed time series models is also constructed in the context of variable selection problems. The setting is very reasonable in the context of financial data analysis and econometrics, and the result is applicable to causality tests of heavy-tailed time series models. In the last two sections, Bartlett-type adjustments for a class of test statistics are discussed when the parameter of interest is on the boundary of the parameter space. A nonlinear adjustment procedure is proposed for a broad range of test statistics including the likelihood ratio, Wald and score statistics.

αž’αŸ†αž–αžΈβ€‹αž’αŸ’αž“αž€αž“αž·αž–αž“αŸ’αž’

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 β€œAsymptotic 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.

αžœαžΆαž™αžαž˜αŸ’αž›αŸƒαžŸαŸ€αžœαž—αŸ…β€‹αž’αŸαž‘αž·αž…αžαŸ’αžšαžΌαž“αž·αž€αž“αŸαŸ‡

αž”αŸ’αžšαžΆαž”αŸ‹αž™αžΎαž„αž’αŸ†αž–αžΈαž€αžΆαžšαž™αž›αŸ‹αžƒαžΎαž‰αžšαž”αžŸαŸ‹αž’αŸ’αž“αž€αŸ”

αž’αžΆαž“β€‹αž–αŸαžαŸŒαž˜αžΆαž“

αž‘αžΌαžšαžŸαž–αŸ’αž‘αž†αŸ’αž›αžΆαžαžœαŸƒ αž“αž·αž„β€‹αžαŸαž”αŸ’αž›αŸαž
αžŠαŸ†αž‘αžΎαž„αž€αž˜αŸ’αž˜αžœαž·αž’αžΈ Google Play Books αžŸαž˜αŸ’αžšαžΆαž”αŸ‹ Android αž“αž·αž„ iPad/iPhone αŸ” αžœαžΆβ€‹αž’αŸ’αžœαžΎαžŸαž˜αž€αžΆαž›αž€αž˜αŸ’αž˜β€‹αžŠαŸ„αž™αžŸαŸ’αžœαŸαž™αž”αŸ’αžšαžœαžαŸ’αžαž·αž‡αžΆαž˜αž½αž™β€‹αž‚αžŽαž“αžΈβ€‹αžšαž”αžŸαŸ‹αž’αŸ’αž“αž€β€‹ αž“αž·αž„β€‹αž’αž“αž»αž‰αŸ’αž‰αžΆαžαž±αŸ’αž™β€‹αž’αŸ’αž“αž€αž’αžΆαž“αž–αŸαž›β€‹αž˜αžΆαž“αž’αŸŠαžΈαž“αž’αžΊαžŽαž·αž αž¬αž‚αŸ’αž˜αžΆαž“β€‹αž’αŸŠαžΈαž“αž’αžΊαžŽαž·αžβ€‹αž“αŸ…αž‚αŸ’αžšαž”αŸ‹αž‘αžΈαž€αž“αŸ’αž›αŸ‚αž„αŸ”
αž€αž»αŸ†αž–αŸ’αž™αžΌαž‘αŸαžšβ€‹αž™αž½αžšαžŠαŸƒ αž“αž·αž„αž€αž»αŸ†αž–αŸ’αž™αžΌαž‘αŸαžš
αž’αŸ’αž“αž€αž’αžΆαž…αžŸαŸ’αžŠαžΆαž”αŸ‹αžŸαŸ€αžœαž—αŸ…αž‡αžΆαžŸαŸ†αž‘αŸαž„αžŠαŸ‚αž›αž”αžΆαž“αž‘αž·αž‰αž“αŸ…αž€αŸ’αž“αž»αž„ Google Play αžŠαŸ„αž™αž”αŸ’αžšαžΎαž€αž˜αŸ’αž˜αžœαž·αž’αžΈαžšαž»αž€αžšαž€αžαžΆαž˜αž’αŸŠαžΈαž“αž’αžΊαžŽαž·αžαž€αŸ’αž“αž»αž„αž€αž»αŸ†αž–αŸ’αž™αžΌαž‘αŸαžšαžšαž”αžŸαŸ‹αž’αŸ’αž“αž€αŸ”
eReaders αž“αž·αž„β€‹αž§αž”αž€αžšαžŽαŸβ€‹αž•αŸ’αžŸαŸαž„β€‹αž‘αŸ€αž
αžŠαžΎαž˜αŸ’αž”αžΈαž’αžΆαž“αž“αŸ…αž›αžΎβ€‹αž§αž”αž€αžšαžŽαŸ e-ink αžŠαžΌαž…αž‡αžΆβ€‹αž§αž”αž€αžšαžŽαŸαž’αžΆαž“β€‹αžŸαŸ€αžœαž—αŸ…αž’αŸαž‘αž·αž…αžαŸ’αžšαžΌαž“αž·αž€ Kobo αž’αŸ’αž“αž€αž“αžΉαž„αžαŸ’αžšαžΌαžœβ€‹αž‘αžΆαž‰αž™αž€β€‹αž―αž€αžŸαžΆαžš αž αžΎαž™β€‹αž•αŸ’αž‘αŸαžšαžœαžΆαž‘αŸ…β€‹αž§αž”αž€αžšαžŽαŸβ€‹αžšαž”αžŸαŸ‹αž’αŸ’αž“αž€αŸ” αžŸαžΌαž˜αž’αž“αž»αžœαžαŸ’αžαžαžΆαž˜β€‹αž€αžΆαžšαžŽαŸ‚αž“αžΆαŸ†αž›αž˜αŸ’αž’αž·αžαžšαž”αžŸαŸ‹αž˜αž‡αŸ’αžˆαž˜αžŽαŸ’αžŒαž›αž‡αŸ†αž“αž½αž™ αžŠαžΎαž˜αŸ’αž”αžΈαž•αŸ’αž‘αŸαžšαž―αž€αžŸαžΆαžšβ€‹αž‘αŸ…αž§αž”αž€αžšαžŽαŸαž’αžΆαž“αžŸαŸ€αžœαž—αŸ…β€‹αž’αŸαž‘αž·αž…αžαŸ’αžšαžΌαž“αž·αž€αžŠαŸ‚αž›αžŸαŸ’αž‚αžΆαž›αŸ‹αŸ”