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Goodness-of-Fit Tests for Linear and Nonlinear Time Series Models
| Content Provider | Scilit |
|---|---|
| Author | Escanciano, J. Carlos |
| Copyright Year | 2006 |
| Description | In this article we study a general class of goodness-of-fit tests for a parametric conditional mean of a linear or nonlinear time series model. Among the properties of the proposed tests are that they are suitable when the conditioning set is infinite-dimensional; that they are consistent against a broad class of alternatives, including Pitman's local alternatives converging at the parametric rate n−1/2, with n the sample size; and that they do not need to choose a lag order depending on the sample size or to smooth the data. It turns out that the asymptotic null distributions of the tests depend on the data generating process, so a new bootstrap procedure is proposed and theoretically justified. The proposed bootstrap tests are robust to higher-order dependence, particularly to conditional heteroscedasticity of unknown form. A simulation study compares the finite-sample performance of the proposed and competing tests and shows that our tests can play a valuable role in time series modeling. Finally, an application to an economic price series highlights the merits of our approach. |
| Related Links | http://www.unav.edu/documents/10174/6546776/1132757196_wp0205.pdf |
| Ending Page | 541 |
| Page Count | 11 |
| Starting Page | 531 |
| ISSN | 01621459 |
| e-ISSN | 1537274X |
| DOI | 10.1198/016214505000001050 |
| Journal | Journal of the American Statistical Association |
| Issue Number | 474 |
| Volume Number | 101 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2006-06-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Journal of the American Statistical Association Soil Science Goodness Nonlinear Bootstrap Time Series Modeling Parametric Fit Sample Size Order Dependence |
| Content Type | Text |
| Resource Type | Article |
| Subject | Statistics and Probability Statistics, Probability and Uncertainty |