Taniguchi / Pierret / Schumann Econometrics, Finance, and Time Series Analysis

Erscheinungsjahr 2026
ISBN: 978-981-958045-3
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 124 Seiten

Reihe: SpringerBriefs in Statistics

This book provides a new contemporary time series approach for econometrics and finance. In a concrete manner a very general divergence between spectra is introduced, resulting in the development of a statistical inference that is efficient and robust, and leads to a new perspective. A measure of systemic risk is also developed in the energy market,which quantifies the cost of energy asset distress vis-à-vis the broader economy during crises, and examines the dynamic interaction between solvency and funding liquidity risk in banks using a panel vector autoregressive (VAR) model. This step shows that a forward-looking measure of capital shortfall under stress is both a predictor and an outcome of funding liquidity risk. Additionally, a new integrated likelihood-based approach for estimating nonlinear panel data models is described. Unlike existing integrated likelihoods, the new integrated likelihood is closer to a genuine likelihood. The book explains why this is due to first-order information unbiasedness, and why it seems to matter more for inference than for estimation. Results of studies in econometrics are provided for support.

Zielgruppe

Research

Autoren/Hrsg.

Taniguchi, Masanobu

Pierret, Diane

Schumann, Martin

Severini, Thomas A.

Tripathi, Gautam

Xue, Yujie

Inhaltsverzeichnis

1 Introduction.- 2 Hellinger Distance Estimation for Non-Regular Spectra.- 3 Local Whittle likelihood approach for generalized divergence.- 4 Systemic Risk in Energy Markets: Measuring Co-Movements in Energy Asset Prices During Crises.- 5 Modeling Solvency and Liquidity Interactions in Banking: A Panel VAR Analysis.- 6 Integrated likelihood based inference for nonlinear panel data models.- 7 Reducing score and information bias in panel data likelihoods.- 8 Shrinkage estimators of BLUE for time series regression models.

Über Autor(innen)

Masanobu Taniguchi received the B.S. degree in mathematics and the M.S. and Dr. degrees in mathematical science from Osaka University, Japan, in 1974, 1976 and 1981, respectively. He joined the Department of Mathematics, Hiroshima University, and the Department of Mathematical Science, Osaka University, in 1983 and 1990, respectively. He was a Visiting Professor at the University of Bristol ,UK, in 2000. He was a Professor in the Department of Applied Mathematics, Waseda University, Japan. 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 - ), and acted the Editor of the Journal of the Japan Statistical Society (2006 - 2009). From 2011, he was a Research Importance Professor at the Research Institute for Science & Engineering, Waseda University. From 2022, he is an emeritus professor of Waseda University, Japan.

Diane Pierret is Assistant Professor at the Department of Finance at the University of Luxembourg and a Research Affiliate of the Centre for Economic Policy Research (CEPR). Prior to joining the University of Luxembourg, Prof. Pierret was Assistant Professor at the University of Lausanne and Faculty Member of the Swiss Finance Institute. She worked before at the Volatility Institute at NYU Stern School of Business and obtained her PhD in Statistics from the Université Catholique de Louvain. Her research focuses on financial intermediation, liquidity risk, systemic risk, regulation, and monetary policy.

Martin Schumann is Assistant Professor of econometrics at Maastricht University. He has obtained his PhD in econometrics from the University of Luxembourg. Prior to his current position, he has been postdoctoral researcher at the TU Dortmund and the Ruhr- University Bochum. His research focuses on estimation and inference in panel data models with unobserved heterogeneity.

Thomas A. Severini is a Professor of Statistics and Data Science at Northwestern University and a fellow of the American Statistical Association and the Institute of Mathematical Statistics. He earned his PhD in Statistics from the University of Chicago. His research areas include likelihood inference, nonparametric and semiparametric methods, and applications to econometrics.

Gautam Tripathi is Professor of Econometrics at the Department of Economics and Management, University of Luxembourg. He earned his PhD in Economics from Northwestern University. His research areas are microeconometrics and econometric theory.

Yujie Xue received a B.S. degree from Peking University, China, in 2013 and received the M.S. and D.S. degrees from Waseda University, Japan in 2015 and 2021. She joined the School of International Liberal Studies, Waseda University, and Research Institute for Science and Engineering, Waseda University, in 2018 and 2021 respectively. She is currently a project assistant professor of Risk Analysis Research Center, The Institute of Statistical Mathematics, Japan. She mainly focuses on research in frequency domain for stationary time series, like the estimation of spectral density, variable selection problem with long-memory disturbances.