Bootstrap forecast of multivariate VAR models without using the backward representation
AbstractIn this paper, we show how to simplify the construction of bootstrap prediction densities in multivariate VAR models by avoiding the backward representation. Bootstrap prediction densities are attractive because they incorporate the parameter uncertainty a any particular assumption about the error distribution. What is more, the construction of densities for more than one-step unknown asymptotically. The main advantage of the new simple without loosing the good performance of bootstrap procedures. Furthermore, by avoiding a backward representation, its asymptotic validity can be proved without relying on the assumption of Gaussian errors as proposed in this paper can be implemented to obtain prediction densities in models without a backward representation as, for example, models with MA components or GARCH disturbances. By comparing the finite sample performance of the proposed procedure with those of alternatives, we show that nothing is lost when using it. Finally, we implement the procedure to obtain prediction regions for US quarterly future inflation, unemployment and GDP growth
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Bibliographic InfoPaper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws113426.
Date of creation: Oct 2011
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Non-Gaussian VAR models; Prediction cubes; Prediction density; Prediction regions; Prediction ellipsoids; Resampling methods;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-11-07 (All new papers)
- NEP-ECM-2011-11-07 (Econometrics)
- NEP-ETS-2011-11-07 (Econometric Time Series)
- NEP-FOR-2011-11-07 (Forecasting)
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