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Open AccessArticle

Identifying the Most Relevant Lag with Runs

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Departamento de Métodos Cuantitativos para la Economía y la Empresa, Universidad de Murcia, Espinardo 30100, Spain
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Departamento de Economía A. Cuantitativa I, Universidad Nacional de Educación a Distancia (UNED), Madrid 28040, Spain
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Department of Quantitative Methods, Universidad Politécnica de Cartagena, Cartagena 30203, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Carlos Alberto De Bragança Pereira and Adriano Polpo
Entropy 2015, 17(5), 2706-2722; https://doi.org/10.3390/e17052706
Received: 19 February 2015 / Revised: 19 April 2015 / Accepted: 23 April 2015 / Published: 28 April 2015
(This article belongs to the Special Issue Inductive Statistical Methods)
In this paper, we propose a nonparametric statistical tool to identify the most relevant lag in the model description of a time series. It is also shown that it can be used for model identification. The statistic is based on the number of runs, when the time series is symbolized depending on the empirical quantiles of the time series. With a Monte Carlo simulation, we show the size and power performance of our new test statistic under linear and nonlinear data generating processes. From the theoretical point of view, it is the first time that symbolic analysis and runs are proposed to identifying characteristic lags and also to help in the identification of univariate time series models. From a more applied point of view, the results show the power and competitiveness of the proposed tool with respect to other techniques without presuming or specifying a model. View Full-Text
Keywords: delay time; runs tests; symbolic analysis delay time; runs tests; symbolic analysis
MDPI and ACS Style

Faura, Ú.; Lafuente, M.; Matilla-García, M.; Ruiz, M. Identifying the Most Relevant Lag with Runs. Entropy 2015, 17, 2706-2722.

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