Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/247510 
Year of Publication: 
2019
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 7 [Issue:] 1 [Publisher:] MDPI [Place:] Basel [Year:] 2019 [Pages:] 1-16
Publisher: 
MDPI, Basel
Abstract: 
The focus of this paper is an information theoretic-symbolic logic approach to extract information from complex economic systems and unlock its dynamic content. Permutation Entropy (PE) is used to capture the permutation patterns-ordinal relations among the individual values of a given time series; to obtain a probability distribution of the accessible patterns; and to quantify the degree of complexity of an economic behavior system. Ordinal patterns are used to describe the intrinsic patterns, which are hidden in the dynamics of the economic system. Empirical applications involving the Dow Jones Industrial Average are presented to indicate the information recovery value and the applicability of the PE method. The results demonstrate the ability of the PE method to detect the extent of complexity (irregularity) and to discriminate and classify admissible and forbidden states.
Subjects: 
Cressie-Read divergence
information theoretic methods
complexity
nonparametric econometrics
permutation entropy
nonlinear time series
symbolic logic
JEL: 
C13
C14
C25
C51
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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