Template-type: ReDIF-Paper 1.0
Author-Name: Chia-Lin Chang
Author-Workplace-Name: Department of Applied Economics and Department of Finance National Chung Hsing University, Taiwan.
Author-Name: Michael McAleer
Author-Workplace-Name: Department of Quantitative Finance National Tsing Hua University, Taiwan and Econometric Institute Erasmus School of 
	Economics Erasmus University Rotterdam, The Netherlands and Department of Quantitative Economics Complutense University of 
	Madrid, Spain And Institute of Advanced Sciences Yokohama National University, Japan.
Author-Name: Wing-Keung Wong
Author-Workplace-Name: Department of Finance, Fintech Center, and Big Data Research Center, Asia University, Taiwan and Department of Medical Research China 
	Medical University Hospital And Department of Economics and Finance Hang Seng Management College, Hong Kong, China and Department of Economics, Lingnan 
	University, Hong Kong, China.
Title: Decision Sciences, Economics, Finance, Business, Computing, and Big Data: Connections
Abstract: 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.
Classification-JEL: A10, G00, G31, O32.
Keywords: Decision sciences; Economics; Finance; Business; Computing; Big data; theoretical models; Econometric and statistical models; Applications.
Length: 59 pages 
Creation-Date: 2018-03
Number: 2018-09
X-File-Ref: http://america.sim.ucm.es/repec/ucm/ref/doicae1809.txt
File-URL: https://eprints.ucm.es/id/eprint/48454/1/1809.pdf
File-Format: Application/pdf
Handle: RePEc:ucm:doicae:1809