Threshold quantile autoregressive models

A. F. Galvao , Gabriel Montes-Rojas (gabriel.montes@fce.uba.ar) and Jose Olmo

Working Papers from Department of Economics, City University London

Abstract: We study in this article threshold quantile autoregressive processes. In particular we propose estimation and inference of the parameters in nonlinear quantile processes when the threshold parameter defining nonlinearities is known for each quantile, and also when the parameter vector is estimated consistently. We derive the asymptotic properties of the nonlinear threshold quantile autoregressive estimator. In addition, we develop hypothesis tests for detecting threshold nonlinearities in the quantile process when the threshold parameter vector is not identified under the null hypothesis. In this case we propose to approximate the asymptotic distribution of the composite test using a p-value transformation. This test contributes to the literature on nonlinearity tests by extending Hansen’s (Econometrica 64, 1996, pp.413-430) methodology for the conditional mean process to the entire quantile process. We apply the proposed methodology to model the dynamics of US unemployment growth after the Second World War. The results show evidence of important heterogeneity associated with unemployment, and strong asymmetric persistence on unemployment growth.

Keywords: nonlinear models; quantile regression; threshold models (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Threshold quantile autoregressive models (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:cty:dpaper:09/05

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