Time-Varying Transition Probability Matrix Estimation and Its Application to Brand Share Analysis.

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ID: 70114
2017
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Abstract
In a product market or stock market, different products or stocks compete for the same consumers or purchasers. We propose a method to estimate the time-varying transition matrix of the product share using a multivariate time series of the product share. The method is based on the assumption that each of the observed time series of shares is a stationary distribution of the underlying Markov processes characterized by transition probability matrices. We estimate transition probability matrices for every observation under natural assumptions. We demonstrate, on a real-world dataset of the share of automobiles, that the proposed method can find intrinsic transition of shares. The resulting transition matrices reveal interesting phenomena, for example, the change in flows between TOYOTA group and GM group for the fiscal year where TOYOTA group's sales beat GM's sales, which is a reasonable scenario.
Reference Key
chiba2017timevaryingplos Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chiba, Tomoaki;Hino, Hideitsu;Akaho, Shotaro;Murata, Noboru;
Journal PloS one
Year 2017
DOI
10.1371/journal.pone.0169981
URL
Keywords
Hypoxia fisheries bioeconomics coupled human-natural systems spatial dynamics

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