Using Bayesian Statistics in Enterprise Demography
DOI:
https://doi.org/10.15678/ZNUEK.2015.0947.1105Keywords:
Bayesian point estimation, mean square error, conjugate family, prior distribution, posterior distribution, number of enterprise birthsAbstract
Knowledge of the number of different kinds of enterprises that will be created in a coming year is essential information. It can be used in macroeconomic analyses and as a constituent of the background for economic policy.
From a demographics point of view, we consider the creation (birth) of some enterprise as a basic indicator. It can also be approached from the point of view of inference, as the creation of enterprise is influenced by a wide variety of inputs. Enterprise creation may therefore be thought of as a random process.
The analytic tools Bayesian statistics provide make it possible involve more kinds of information into statistical analysis and gradually update the parameter estimations. We used the conjugate family Poisson / gamma to estimate the number of enterprises to be created in a coming year. The considerations were concerned with the mean square error, which was used as the main criterion of the point estimation quality. We solved two kinds of problems: to find a Bayesian point estimation that has a smaller mean square error than the classical one in a predetermined interval, and, along with it, to model prior information in a very simple way.
In finding some connection among the variables contained in the conjugate family Poisson / gamma, we solved both presented problems and also developed a simple algorithm for optimal point estimation of the Poisson distribution parameter. This algorithm was used to estimate the number of enterprises created.
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