Innovation, growth and aggregate volatility from a Bayesian nonparametric perspective
| Autor: | Antonio Lijoi, Igor Prünster, Pietro Muliere, Filippo Taddei |
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| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
Stylized fact aggregate volatility 010102 general mathematics Probability and statistics AGGREGATE VOLATILITY ASYMPTOTICS BAYESIAN NONPARAMETRICS ECONOMIC GROWTH POISSON-DIRICHLET PROCESS STATISTICS AND PROBABILITY Growth model economic growth 01 natural sciences Bayesian nonparametrics 010104 statistics & probability asymptotics Poisson-Dirichlet process 91B62 Econometrics 60G57 62F15 0101 mathematics Statistics Probability and Uncertainty Volatility (finance) Mathematical economics Mathematics |
| Zdroj: | Electron. J. Statist. 10, no. 2 (2016), 2179-2203 |
| ISSN: | 1935-7524 |
| DOI: | 10.1214/16-ejs1165 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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