Next Article in Journal
Finite Key Size Analysis of Two-Way Quantum Cryptography
Next Article in Special Issue
A Robust Bayesian Approach to an Optimal Replacement Policy for Gas Pipelines
Previous Article in Journal
A Fuzzy Logic-Based Approach for Estimation of Dwelling Times of Panama Metro Stations
Article Menu

Export Article

Open AccessArticle
Entropy 2015, 17(5), 2706-2722; doi:10.3390/e17052706

Identifying the Most Relevant Lag with Runs

1
Departamento de Métodos Cuantitativos para la Economía y la Empresa, Universidad de Murcia, Espinardo 30100, Spain
2
Departamento de Economía A. Cuantitativa I, Universidad Nacional de Educación a Distancia (UNED), Madrid 28040, Spain
3
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
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)
View Full-Text   |   Download PDF [1018 KB, uploaded 28 April 2015]   |  

Abstract

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
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top