Identifying the Most Relevant Lag with Runs

Entropy

Úrsula Faura⁴  Matilde Lafuente⁴  Mariano Matilla-Garc໚³  Manuel Ruiz²  Carlos Alberto De Bragan๺ Pereira¹ 

[1] Departamento de Métodos Cuantitativos para la Economía y la Empresa, Universidad de Murcia, Espinardo 30100, Spain;;Department of Quantitative Methods, Universidad Politécnica de Cartagena, Cartagena 30203, Spain; E-Mail:;Departamento de Economía A. Cuantitativa I, Universidad Nacional de Educación a Distancia (UNED), Madrid 28040, Spain;Departamento de Métodos Cuantitativos para la Economía y la Empresa, Universidad de Murcia, Espinardo 30100, Spain; E-Mails:

关键词: delay time;    runs tests;    symbolic analysis;   

DOI  :  10.3390/e17052706

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland