GCov-Based Portmanteau Test
| Autor: | Jasiak, Joann, Neyazi, Aryan Manafi |
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| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | We examine finite sample performance of the Generalized Covariance (GCov) residual-based specification test for semiparametric models with i.i.d. errors. The residual-based multivariate portmanteau test statistic follows asymptotically a $\chi^2$ distribution when the model is estimated by the GCov estimator. The test is shown to perform well in application to the univariate mixed causal-noncausal MAR, double autoregressive (DAR) and multivariate Vector Autoregressive (VAR) models. We also introduce a bootstrap procedure that provides the limiting distribution of the test statistic when the specification test is applied to a model estimated by the maximum likelihood, or the approximate or quasi-maximum likelihood under a parametric assumption on the error distribution. Comment: 49 pages, 2 figures |
| Databáze: | arXiv |
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