A Cobb–Douglas type model with stochastic restrictions: formulation, local influence diagnostics and data analytics in economics

Francisco J.A. Cysneiros, Víctor Leiva, Shuangzhe Liu, Carolina Marchant, Paulo Scalco

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

We propose a methodology for modelling and influence diagnostics in a Cobb–Douglas type setting. This methodology is useful for describing case-studies from economics. We consider stochastic restrictions for the model based on auxiliary information in order to improve its predictive ability. Model errors are assumed to follow the family of symmetric distributions and particularly its normal and Student-t members. We estimate the model parameters with the maximum likelihood method, which allows us to compare the normal case with a flexible framework that provides robust estimation of parameters based on the Student-t case. To conduct diagnostics in the model, we use two approaches for studying how a perturbation may affect on the mixed estimation procedure of its parameters due to the usage of sample data and non-sample auxiliary information. Curvatures and slopes used to detect local influence with both approaches are derived, considering perturbation schemes of case-weight, response and explanatory variables. Numerical evaluation of the proposed methodology is performed by Monte Carlo simulations and by applications with two data sets from economics, all of which show its good performance and its further applications. Particularly, the real data analyses confirm the importance of statistical diagnostics in the data modelling.

Original languageEnglish
Pages (from-to)1693-1719
Number of pages27
JournalQuality and Quantity
Volume53
Issue number4
DOIs
Publication statusPublished - 15 Jul 2019

Fingerprint Dive into the research topics of 'A Cobb–Douglas type model with stochastic restrictions: formulation, local influence diagnostics and data analytics in economics'. Together they form a unique fingerprint.

  • Cite this