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2016 Innovation, growth and aggregate volatility from a Bayesian nonparametric perspective
Antonio Lijoi, Pietro Muliere, Igor Prünster, Filippo Taddei
Electron. J. Statist. 10(2): 2179-2203 (2016). DOI: 10.1214/16-EJS1165

Abstract

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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Antonio Lijoi. Pietro Muliere. Igor Prünster. Filippo Taddei. "Innovation, growth and aggregate volatility from a Bayesian nonparametric perspective." Electron. J. Statist. 10 (2) 2179 - 2203, 2016. https://doi.org/10.1214/16-EJS1165

Information

Received: 1 November 2015; Published: 2016
First available in Project Euclid: 19 July 2016

zbMATH: 1346.62060
MathSciNet: MR3528712
Digital Object Identifier: 10.1214/16-EJS1165

Subjects:
Primary: 60G57, 62F15, 91B62

Rights: Copyright © 2016 The Institute of Mathematical Statistics and the Bernoulli Society

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