The discrete Threshold Regression (TR) model describes a simple form of nonlinear regression featuring piecewise linear specifications and regime switching that occurs when an observed variable crosses unknown thresholds. TR specifications are quite popular as they are easy to estimate and interpret, and able to produce interesting nonlinearities and rich dynamics. Among the applications of TR are models for sample splitting, multiple equilibria, and the very popular Threshold Autoregression (TAR) and self-exciting Threshold Autoregression (SETAR) specifications (Hansen 1999, 2011; Potter 2003).

This chapter describes tools for estimating TR models with known or unknown thresholds. Among the powerful features are model selection tools for selecting the best threshold variable from a candidate list, and the ability to specify both regime varying and non-varying variables. You may, for example, easily specify a two-regime SETAR model and allow EViews to estimate the optimal delay parameter, threshold values, and coefficients and covariance estimates for the varying and regression parameters.