Decision Sciences, Economics, Finance, Business, Computing, and Big Data: Connections
Author(s) -
ChiaLin Chang,
Michael McAleer,
WingKeung Wong
Publication year - 2018
Publication title -
ssrn electronic journal
Language(s) - English
Resource type - Journals
ISSN - 1556-5068
DOI - 10.2139/ssrn.3140371
Subject(s) - estimator , big data , cognate , computational statistics , predictive power , econometrics , management science , computer science , estimation , data science , economics , finance , mathematics , statistics , management , data mining , machine learning , linguistics , philosophy , epistemology
This paper provides a review of some connecting literature in Decision Sciences, Economics, Finance, Business, Computing, and Big Data. We then discuss some research that is related to the six cognate disciplines. Academics could develop theoretical models and subsequent econometric and statistical models to estimate the parameters in the associated models. Moreover, they could then conduct simulations to examine whether the estimators or statistics in the new theories on estimation and hypothesis have small size and high power. Thereafter, academics and practitioners could then apply their theories to analyze interesting problems and issues in the six disciplines and other cognate areas.
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