Abstract
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.
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References
de Peretti, C., Siani, C., Cerrato, M.: An artificial neural network based heterogeneous panel unit root test in case of cross sectional independence. In: 2009 International Joint Conference on Neural Network (IJCNN 2009), Atlanta, June 14-19. International Neural Network Society, IEEE Computational Intelligence Society (Forthcoming, 2009)
Abuaf, N., Jorion, P.: Purchasing power parity in the long run. Journal of Finance 45, 157–174 (1990)
Im, K., Pesaran, M.H., Shin, Y.: Testing for unit roots in heterogeneous panels. Unpublished manuscript, University of Cambridge (1997), http://www.econ.cam.ac.k/faculty/pesaran
Mills, T.: The Econometric Modelling of Financial Time Series. Cambridge University Press, Cambridge (1993)
Pesaran, M.H., Potter, S.: A floor and ceiling model of us output. Journal of Economic Dynamics and Control 21(4/5), 661–696 (1997)
Im, K., Pesaran, H., Shin, Y.: Testing for unit roots in heterogeneous panels. Journal of Econometrics 115, 53–74 (2003)
Blake, A.P., Kapetanios, G.: Pure significance tests of the unit root hypothesis against nonlinear alternatives. Journal of Time Series Analysis 24(3), 253–267 (2003)
Caner, M., Hansen, B.: Threshold autoregression with a near unit root. Unpublished manuscript, University of Wisconsin (2000)
Kapetanios, G., Shin, Y., Snell, A.: Testing for a unit root in the nonlinear star framework. Journal of Econometrics 112, 359–379 (2003)
Kapetanios, G., Shin, Y.: Testing for a unit root against threshold nonlinearity. Unpublished manuscript, University of Edinburgh (2000)
Blake, A.P., Kapetanios, G.: A radial basis function artificial neural network test for arch. Economics Letters 69(1), 15–25 (2000)
Blake, A.P., Kapetanios, G.: A radial basis function artificial neural network test for neglected nonlinearity. Working Paper, National Institute of Economic and Social Research (2000)
Campbell, J.Y., Lo, A.W., MacKinlay, A.C.: The Econometrics of Financial Markets. Princeton University Press, Princeton (1997)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Basawa, I.V., Mallick, A.K., McCormick, W.P., Reeves, J.H., Taylor, R.L.: Bootstrapping unstable first order autoregressive processes. Annals of Statistics 19(2), 1098–1101 (1991)
Murray, C.J., Papell, D.H.: The purchasing power persistence paradigm. Journal of International Economics 56, 1–19 (2002)
Murray, C.J., Papell, D.H.: The purchasing power puzzle is worse than you think. Empirical Economics 30(3), 783–790 (2005)
Cerrato, M., de Peretti, C., Sarantis, N.: A nonlinear panel unit root test under cross section dependence. Working paper. Centre for International Capital Markets, London Metropolitan Business School, London Metropolitan University (2007)
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de Peretti, C., Siani, C., Cerrato, M. (2009). A Bootstrap Artificial Neural Network Based Heterogeneous Panel Unit Root Test in Case of Cross Sectional Independence. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10677-4_50
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DOI: https://doi.org/10.1007/978-3-642-10677-4_50
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