Self-Similar Measures in Multi-Sector Endogenous Growth Models

Davide La Torre, Simone Marsiglio, Franklin Mendivil () and Fabio Privileggi

Department of Economics and Statistics Cognetti de Martiis. Working Papers from University of Turin

Abstract: We analyze two types of stochastic discrete time multi-sector endogenous growth models, namely a basic Lucas-Uzawa (1988) model and an extended three-sector version as in La Torre and Marsiglio (2010). As in the case of sustained growth the optimal dynamics of the state variables are not stationary, we focus on the dynamics of the capital ratio variables, and we show that, through appropriate log-transform ations, they can be converted into affine iterated function systems converging to an invariant distribution supported on some fractal set. This proves that also the steady state of endogenous growth models i.e., the stochastic balanced growth path equilibrium—might have a fractal nature. We also provide some sufficient conditions under which the associated self-similar measures turn out to be either singular or absolutely continuous (for the three-sector model we only consider the singularity).

Pages: 30 pages
Date: 2015-02
New Economics Papers: this item is included in nep-gro
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.est.unito.it/do/home.pl/Download?doc=/a ... 015dip/wp_9_2015.pdf (application/pdf)

Related works:
Journal Article: Self-similar measures in multi-sector endogenous growth models (2015)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:uto:dipeco:201509

Access Statistics for this paper

More papers in Department of Economics and Statistics Cognetti de Martiis. Working Papers from University of Turin Contact information at EDIRC.
Bibliographic data for series maintained by Laura Ballestra () and Cinzia Carlevaris ().