Nonparametric Estimation of Generalized Impulse Response Functions
AbstractWe derive a local linear estimator of generalized impulse response (GIR) functions for nonlinear conditional heteroskedastic autoregressive processes and show its asymptotic normality. We suggest a plug-in bandwidth based on the derived asymptotically optimal bandwidth. A local linear estimator for the conditional variance function is proposed which has simpler bias than the standard estimator. This is achieved by appropriately eliminating the conditional mean. Alternatively to the direct local linear estimators of the k-step prediction functions which enter the GIR estimator we suggest to use multi-stage prediction techniques. In a small simulation experiment the latter estimator is found to perform best.
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Bibliographic InfoPaper provided by Econometric Society in its series Econometric Society World Congress 2000 Contributed Papers with number 1417.
Date of creation: 01 Aug 2000
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Other versions of this item:
- Tschernig, Rolf & Yang, Lijian, 2000. "Nonparametric estimation of generalized impulse response function," SFB 373 Discussion Papers 2000,89, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
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