SETAR

Abstract: This is a preliminary report on a newly developed simple and practical procedure of statistical identification of predictors by using autoregressive models. The use of autoregressive representation of a stationary time series (or the innovations approach) in the analysis of time series has recently been attracting attentions of many research workers and it is expected that this time domain approach will give answers to many problems, such as the identification of noisy feedback systems, which could not be solved by the direct application of frequency domain approach [1], [2], [3], [9].

Abstract: This article considers the application of two families of nonlinear autoregressive models, the logistic (LSTAR) and exponential (ESTAR) autoregressive models. This includes the specification of the model based on simple statistical tests: linearity testing against smooth transition autoregression, determining the delay parameter and choosing between LSTAR and ESTAR models are discussed. Estimation by nonlinear least squares is considered as well as evaluating the properties of the estimated model. The proposed techniques are illustrated by examples using both simulated and real time series.

Abstract: Cross-sectional spatial models frequently contain a spatial lag of the dependent variable as a regressor or a disturbance term that is spatially autoregressive. In this article we describe a computationally simple procedure for estimating cross-sectional models that contain both of these characteristics. We also give formal large-sample results.

Abstract: Recent work by Said and Dickey (1984 ,1985) , Phillips (1987), and Phillips and Perron(1988) examines tests for unit roots in the autoregressive part of mixed autoregressive-integrated-moving average (ARIHA) models (tests for stationarity). Monte Carlo experiments show that these unit root tests have different finite sample distributions than the unit root tests developed by Fuller(1976) and Dickey and Fuller (1979, l981) for autoregressive processes. In particular, the tests developed by Philllps (1987) and Phillips and Perron (1988) seem more sensitive to model misspeciflcation than the high order autoregressive approximation suggested by Said and Diekey(1984).