In this article, we develop a general method for testing threshold effects in regression models, using sup-likelihood-ratio (LR)-type statistics. Although the sup-LR-type test statistic has been considered in the literature, our method for establishing the asymptotic null distribution is new and nonstandard. The standard approach in the literature for obtaining the asymptotic null distribution ...

We examine the effect of public debt on private investment in selected emerging economies. Using a panel threshold regression model, we estimate value about 3 percent, average, below which stimulates investment. Our additional analysis involving developed economies suggests that crowding out is less evident relative to as higher stocks do not seem significantly undermine their investments. Thes...

This paper analyses the threshold effect of natural resources on internal conflict in 124 countries over period 1984–2017 using panel smooth transition regression approach. Data used this study is obtained from International Country Risk Guide and World Development Indicator. The results suggest that non-linear. Moreover, highlights fact fueling far being conclusive. Nevertheless, also indicate...

This paper studies the relationship between public investment and private in a sample of 21 Organization for Economic Cooperation Development (OECD) countries 2000 2019. Using panel data nonlinear threshold regression models, empirical results show that there exist levels share investment, real Gross Domestic Product (GDP) growth rate interest affect investment. All estimates support crowding-i...

First hitting time models are a technique of modeling a stochastic process as it approaches or avoids a boundary, also known as a threshold. The process itself may be unobservable, making this a difficult problem. Regression techniques, however, can be employed to model the data as it compares to the threshold, creating a class of first hitting time models called threshold regression models. Su...

The present work describes a simple approach to estimating the location of a threshold/change point in a nonparametric regression. This model has connections both to the time-series and regression discontinuity literatures. The estimator leverages a simple decomposition, giving it the form of a semiparametric smooth coefficient model. Optimal bandwidth selection and a suite of testing facilitie...

We formulate a statistical model for the regulation of global gene expression by multiple regulatory programs and propose a thresholding singular value decomposition (T-SVD) regression method for learning such a model from data. Extensive simulations demonstrate that this method offers improved computational speed and higher sensitivity and specificity over competing approaches. The method is u...