A Practical, Accurate, Information Criterion for Nth Order Markov Processes

Barde, Sylvain (2016) A Practical, Accurate, Information Criterion for Nth Order Markov Processes. Computational Economics, . ISSN 0927-7099. (doi:https://doi.org/10.1007/s10614-016-9617-9) (Full text available)

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Abstract

The recent increase in the breath of computational methodologies has been matched with a corresponding increase in the difficulty of comparing the relative explanatory power of models from different methodological lineages. In order to help address this problem a Markovian information criterion (MIC) is developed that is analogous to the Akaike information criterion (AIC) in its theoretical derivation and yet can be applied to any model able to generate simulated or predicted data, regardless of its methodology. Both the AIC and proposed MIC rely on the Kullback–Leibler (KL) distance between model predictions and real data as a measure of prediction accuracy. Instead of using the maximum likelihood approach like the AIC, the proposed MIC relies instead on the literal interpretation of the KL distance as the inefficiency of compressing real data using modelled probabilities, and therefore uses the output of a universal compression algorithm to obtain an estimate of the KL distance. Several Monte Carlo tests are carried out in order to (a) confirm the performance of the algorithm and (b) evaluate the ability of the MIC to identify the true data-generating process from a set of alternative models.

Item Type: Article
Uncontrolled keywords: AIC, Minimum description length, Markov process, Model selection
Subjects: H Social Sciences > HB Economic Theory
Q Science > QA Mathematics (inc Computing science) > QA297 Numerical analysis
Divisions: Faculties > Social Sciences > School of Economics
Depositing User: Sylvain Barde
Date Deposited: 09 Sep 2016 12:17 UTC
Last Modified: 22 Sep 2016 11:10 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/57155 (The current URI for this page, for reference purposes)
Barde, Sylvain: https://orcid.org/0000-0003-1465-8236
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