Electronic Journal of Statistics

Innovation, growth and aggregate volatility from a Bayesian nonparametric perspective

Antonio Lijoi, Pietro Muliere, Igor Prünster, and Filippo Taddei

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In this paper we consider the problem of uncertainty related to growth through innovations. We study a stylized, although rich, growth model, in which the stochastic innovations follow a Bayesian nonparametric model, and provide the full taxonomy of the asymptotic equilibria. In most cases the variability around the average aggregate behaviour does not vanish asymptotically: this requires to accompany usual macroeconomic mean predictions with some measure of uncertainty, which is readily yielded by the adopted Bayesian nonparametric approach. Moreover, we discover that the extent of the asymptotic variability is the result of the interaction between the rate at which the economy creates new sectors and the concavity of returns in sector specific technologies.

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Electron. J. Statist., Volume 10, Number 2 (2016), 2179-2203.

Received: November 2015
First available in Project Euclid: 19 July 2016

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Zentralblatt MATH identifier

Primary: 62F15: Bayesian inference 60G57: Random measures 91B62: Growth models

Bayesian nonparametrics aggregate volatility asymptotics economic growth Poisson-Dirichlet process


Lijoi, Antonio; Muliere, Pietro; Prünster, Igor; Taddei, Filippo. Innovation, growth and aggregate volatility from a Bayesian nonparametric perspective. Electron. J. Statist. 10 (2016), no. 2, 2179--2203. doi:10.1214/16-EJS1165. https://projecteuclid.org/euclid.ejs/1468954727

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