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Innovation, growth and aggregate volatility from a Bayesian nonparametric perspective

机译:贝叶斯非参数视角的创新,增长和总体波动

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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.
机译:在本文中,我们考虑了与通过创新实现增长相关的不确定性问题。我们研究了一种程式化的,尽管很丰富的增长模型,其中随机创新遵循贝叶斯非参数模型,并提供了渐近均衡的完整分类法。在大多数情况下,围绕平均总体行为的可变性不会渐近消失:这需要伴随通常的宏观经济均值预测并带有一定程度的不确定性,而采用贝叶斯非参数方法很容易得出这一结论。此外,我们发现渐近变化的程度是经济创造新部门的速度与部门特定技术的回报凹面之间相互作用的结果。

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