A MAXENT MODEL FOR MACROSCENARIO ANALYSIS
Simone Landini (),
Corrado Di Guilmi and
Mauro Gallegati
Additional contact information
Simone Landini: IRES Piemonte–Socioeconomic Research Institute of Piedmont, via Nizza 18, 10125, Turin, Italy
Advances in Complex Systems (ACS), 2008, vol. 11, issue 05, 719-744
Abstract:
In this paper, starting from Jaynes' MaxEnt methodology [10, 11], we follow the original idea of Aoki [1] to implement a canonical MaxEnt inference modelfor the replication ofindustrial firms' dynamics over a space of economic states. We develop an aggregate model to infer the distributions of agents at meso level using representative states. In particular, we estimate the access probability for agents in different states consistently with macroscopic economic constraints. The model is calibrated on the basis of a sample of firms, drawn from the AMADEUS database, within the manufacturing industry made up of nine sectors of economic activity from 1995 to 2004, and results come to experimental proof at aggregate macroscopic level.
Keywords: Statistical mechanics; canonical ensemble; MaxEnt; Gibbs distribution; econophysics; Cobb–Douglas technology (search for similar items in EconPapers)
Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S021952590800201X
Access to full text is restricted to subscribers
Related works:
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:wsi:acsxxx:v:11:y:2008:i:05:n:s021952590800201x
Ordering information: This journal article can be ordered from
DOI: 10.1142/S021952590800201X
Access Statistics for this article
Advances in Complex Systems (ACS) is currently edited by Frank Schweitzer
More articles in Advances in Complex Systems (ACS) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().