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A NON‐GAUSSIAN TIME SERIES ANALYSIS OF RURAL RETAIL BUSINESS COUNTS *
Author(s) -
Shonkwiler J. S.,
Harris T. R.
Publication year - 1993
Publication title -
journal of regional science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.171
H-Index - 79
eISSN - 1467-9787
pISSN - 0022-4146
DOI - 10.1111/j.1467-9787.1993.tb00209.x
Subject(s) - poisson distribution , series (stratigraphy) , econometrics , count data , time series , bayes' theorem , state space , computer science , differential (mechanical device) , state vector , statistics , mathematics , bayesian probability , paleontology , physics , classical mechanics , engineering , biology , aerospace engineering
. Determinants of the number of retail firms serving a rural area have been investigated by drawing on demand threshold analysis to analyze cross‐sectional data. The relevance of such studies to an individual community may be limited if it is characterized by differential levels or unique types of economic activity. Alternatively, conducting the analysis using time‐series data on an individual community may lead to problems associated with non‐normal and nonstationary data. These problems are addressed by formulating a state‐space model of time‐series count data. The discrete, nonnegative nature of count data is accommodated by using a conjugate prior for the Poisson location parameter. A guide function is used to link the prior to the state vector and Bayes rules are used for updating.