Dynamic Process Models for the Evaluation of the Compliance Level Evolution: Evidence from Italy
Vincenzo Adamo ()
SN Operations Research Forum, 2021, vol. 2, issue 2, 1-27
Abstract:
Abstract Decision support systems are used in different contexts, including tax policies. In these pages a theoretical analysis is proposed, based on random utility maximization and dynamic process models, to forecast the evolution of the taxable income produced by the application of a rewarding-based compliance methodology recently introduced in Italy. We will briefly describe the econometric characteristics of the new fiscal methodology (SIR); then, we will focus on the theoretical aspects concerning the evolution of the taxable income, in case of both a stochastic and a deterministic process, the latter commonly used to estimate a limit condition of the process (usually a worst-case scenario). The results here obtained can be extended to different kind of tax compliance models and are supposed to be as a basic step to implement an effective IT decision support model. The theoretical connection between SIR and sector studies, the previous compliance methodology, will be discussed. Finally, a numerical example of a deterministic process model based on synthetic data will be provided.
Keywords: Equilibrium; Dynamic systems; Random utility models; Tax compliance (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://link.springer.com/10.1007/s43069-020-00045-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.
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:spr:snopef:v:2:y:2021:i:2:d:10.1007_s43069-020-00045-w
Ordering information: This journal article can be ordered from
https://www.springer.com/journal/43069
DOI: 10.1007/s43069-020-00045-w
Access Statistics for this article
SN Operations Research Forum is currently edited by Marco Lübbecke
More articles in SN Operations Research Forum from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().