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On estimating thresholds in autoregressive models
Kung-Sik Chan,Howell Tong +1 more
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In this article, the problem of estimating the threshold parameter, i.e., the change point, of a threshold autoregressive model is studied by introducing smoothness into the model, sampling properties of the conditional least-squares estimate may be obtained Artificial and real data are used for illustrationsAbstract:
The problem of estimating the threshold parameter, ie, the change point, of a threshold autoregressive model is studied By introducing smoothness into the model, sampling properties of the conditional least-squares estimate may be obtained Artificial and real data are used for illustrationsread more
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Analysis of Financial Time Series
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Inference when a nuisance parameter is not identified under the null hypothesis
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Journal ArticleDOI
Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models
TL;DR: In this article, the authors consider the application of two families of nonlinear autoregressive models, the logistic (LSTAR) and exponential (ESTAR) models, and consider 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.
Journal ArticleDOI
Testing linearity against smooth transition autoregressive models
TL;DR: In this paper, a general univariate smooth transition autoregressive, STAR, model is studied and three tests for testing linearity against STAR models are presented. But the power of the tests in small samples is investigated by simulation when the alternative is the logistic STAR model.
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Applied Nonparametric Regression
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References
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An efficient method for finding the minimum of a function of several variables without calculating derivatives
Journal ArticleDOI
The differentiation of pseudoinverses and nonlinear least squares problems whose variables separate.
Gene H. Golub,Victor Pereyra +1 more
TL;DR: Algorithms are presented which make extensive use of well-known reliable linear least squares techniques, and numerical results and comparisons are given.
The differentiation of pseudo-inverses and non-linear least squares problems whose variables separate.
G. H. GOLUBf,V. Pereyra +1 more
TL;DR: In this paper, the least square fit of nonlinear models of the form {(0t, Yi), l,, m, qgj, ti, and the modified functional r2( 0t (lY O(0 t)/(0)yl)22) is considered.
Journal ArticleDOI
Estimating the transition between two intersecting straight lines
David W. Bacon,Donald G. Watts +1 more
TL;DR: In this paper, a general model is proposed which allows for a smooth transition from one linear regime to the other, accomplished by a curve incorporating a transition parameter, and a Bayesian estimation procedure is used to determine the plausibility of different parameter values.
Journal ArticleDOI
On Conditional Least Squares Estimation for Stochastic Processes
TL;DR: In this paper, an estimation procedure for stochastic processes based on the minimization of a sum of squared deviations about conditional expectations is developed, and the estimators and their limiting covariance matrix are worked out in detail for a subcritical branching process with immigration.