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  • 标题:Innovation, growth and aggregate volatility from a Bayesian nonparametric perspective
  • 本地全文:下载
  • 作者:Antonio Lijoi ; Pietro Muliere ; Igor Prünster
  • 期刊名称:Electronic Journal of Statistics
  • 印刷版ISSN:1935-7524
  • 出版年度:2016
  • 卷号:10
  • 期号:2
  • 页码:2179-2203
  • DOI:10.1214/16-EJS1165
  • 语种:English
  • 出版社:Institute of Mathematical Statistics
  • 摘要: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.
  • 关键词:Bayesian nonparametrics;aggregate volatility, asymptotics;economic growth;Poisson-Dirichlet process.
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