Special Issue of the Journal of Time Series Analysis in Honor of Professor Masanobu Taniguchi

Taniguchi Sensei – our colleague and friend Masanobu Taniguchi – retired from Waseda University in Tokyo at the end of March 2022 after a long and productive career that put Waseda on the international map of time series analysis and mathematical statistics. Masanobu arrived at Waseda from Osaka some 20 years ago and rapidly developed a powerful team of students (in total 19 theses defended) and researchers, as well as an impressive network of international collaborations. Thanks to him and the countless international conferences and symposiums he tirelessly organized all over Japan, numerous statisticians from all continents enjoyed his warm hospitality, established fruitful collaborative contacts with his team, and discovered the refinements of Japanese lifestyle and culture. Statistical inference for stochastic processes and time series is a red thread running through Masanobu’s entire research career. This does not mean, however, that his contributions are narrowly concentrated on one single subject! Quite on the contrary, his scientific interests are embracing an exceptionally wide spectrum of mathematical and applied statistics topics. While it is not possible here to do justice to all of his contributions, let us mention higher-order asymptotics, a notoriously difficult subject where he can be considered to be a worldwide expert, spectral methods, local asymptotic normality and Le Cam’s asymptotic theory of statistical experiments, Edgeworth expansions in stationary processes, estimating functions, discriminant analysis and clustering, empirical likelihood methods, long-memory processes, heavy tails, volatility models, ... not to forget economic and financial applications, risk analysis, and portfolio theory – all in the general framework of serially dependent observations. That activity has resulted in over 150 articles published in internationally acclaimed journals including the Annals of Statistics, the Journal of the Royal Statistical Society, the Journal of the American Statistical Association, Biometrika, the Journal of Econometrics, the Journal of Time Series Analysis, Econometric Theory, the Journal of Multivariate Analysis, among many others, and no less than seven books. It is an honor for us to guest-edit this special issue of the Journal of Time Series Analysis as a tribute to Masanobu’s scientific achievement. This issue contains 12 invited papers, all lying at the frontier in time series analysis research, by econometricians and statisticians. All papers were refereed as per the standards of the journal. Bhattacharjee, Chakraborty and Koul discuss the estimation of the regression parameters in a high-dimensional errors in variables linear regression model, where the measurement errors in the covariates are assumed to form a stationary short-memory moving average process having known Laplace stationary distribution and the regression errors are assumed to be independent nonidentically distributed. They also derive Massart’s inequality for independent and short-memory moving average predictors. Chan and Dai deal with constant parameters testing problem in semi-parametric functional coefficient cointegrated framework. They propose an orthogonal series approximation-based test statistic to tackle the problem, and study its asymptotic theory. The proposed test is illustrated by Monte Carlo simulation and a real data analysis. Davis, Fernandes and Fokianos propose a novel