The globe is being confronted with a tremendous challenge in the form of global warming,
which is produced by emissions of carbon dioxide. To accomplish the double-sided …

Modeling and forecasting symbolic data, especially interval-valued time series (ITS) data,
has received considerable attention in statistics and related fields. The core of available …

This paper introduces the structural threshold regression (STR) model that allows for an
endogenous threshold variable as well as for endogenous regressors. This model provides …

In order to construct prediction intervals without the cumbersome–and typically unjustifiable–
assumption of Gaussianity, some form of resampling is necessary. The regression set-up …

This paper considers robust inference in threshold regression models when the practitioners
do not know whether at the threshold point the true specification has a kink or a jump …

In this paper, we consider the least squares estimation of a panel structure threshold
regression (PSTR) model where both the slope coefficients and threshold parameters may …

In this paper, we introduce an integer-valued threshold autoregressive process, which is
driven by independent negative-binomial distributed random variables and based on …

This paper extends the classical two-regime threshold autoregressive model by introducing
hysteresis to its regime-switching structure, which leads to a new model: the hysteretic …

This paper develops a novel approach for estimating a threshold autoregressive (TAR)
model with multiple-regimes and establishes its large sample properties. By reframing the …

In this paper, we introduce a first-order random coefficient integer-valued threshold
autoregressive process, which is based on binomial thinning. Basic probabilistic and …