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Erschienen in:
2009 | OriginalPaper | Buchkapitel
A Bootstrap Artificial Neural Network Based Heterogeneous Panel Unit Root Test in Case of Cross Sectional Independence
verfasst von : Christian de Peretti, Carole Siani, Mario Cerrato
Erschienen in: Neural Information Processing
Verlag: Springer Berlin Heidelberg
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This paper extends an inference test proposed in [1]. The seminal paper proposes an artificial neural network (ANN) based panel unit root test in a dynamic heterogeneous panel context. The ANN is not complex, but it is not necessarily in the aim of modeling macroeconomic time series. However, it is applied in a difficult mathematical context, in which the classical Gaussian asymptotic probabilistic theory does not apply. Some asymptotic properties for the test were set, however, the small sample properties are not satisfactory. Consequently, in this paper, we propose to use the simulation based numerical method named “bootstrap” to compute the small sample distribution of the test statistics. An application to a panel of bilateral real exchange rate series with the US Dollar from the 20 major OECD countries is provided.