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A nonparametric test of conditional autoregressive heteroscedasticity for threshold autoregressive models
Authors:Min Chen  Gemai Chen
Abstract:Threshold autoregressive models are widely used in time‐series applications. When building or using such a model, it is important to know whether conditional heteroscedasticity exists. The authors propose a nonparametric test of this hypothesis. They develop the large‐sample theory of a test of nonlinear conditional heteroscedasticity adapted to nonlinear autoregressive models and study its finite‐sample properties through simulations. They also provide percentage points for carrying out this test, which is found to have very good power overall.
Keywords:Conditional heteroscedasticity  nonparametric test  threshold autoregressive model  
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