This article reviews some recent developments on the inference of time series data using the
self-normalized approach. We aim to provide a detailed discussion about the use of self …

We propose a new method to construct confidence intervals for quantities that are
associated with a stationary time series, which avoids direct estimation of the asymptotic …

The stochastic gradient descent (SGD) algorithm is widely used for parameter estimation,
especially for huge datasets and online learning. While this recursive algorithm is popular …

The order of integration is valid to characterize linear processes; but it is not appropriate for
non-linear worlds. We propose the concept of summability (a re-scaled partial sum of the …

In this article, we revisit a time series model introduced by MCElroy and Politis (2007a) and
generalize it in several ways to encompass a wider class of stationary, nonlinear, heavy …

This paper considers the problem of variance estimation for the sample mean in the context
of long memory and negative memory time series dynamics, adopting the fixed-bandwidth …

Standard blockwise empirical likelihood (BEL) for stationary, weakly dependent time series
requires specifying a fixed block length as a tuning parameter for setting confidence regions …

The paper reviews the topic of extremal time series. The literature documenting the
presence of extremes in time series data is first reviewed, followed by a discussion of …

This paper studies taper-based estimates of the spectral density utilizing a fixed bandwidth
ratio asymptotic framework, and makes several theoretical contributions:(i) we treat multiple …

The relationship between income inequality and long-run economic growth has gained a
growing attention in economic research for over decades. This study employed advanced …