Template-type: ReDIF-Paper 1.0
Author-Name: Chia-Lin Chang
Author-Workplace-Name: Department of Applied economics, Department of Finance National Chung Hsing University, Taiwan.

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, Taiwan And Department of Economics and Finance, Hang Seng Management College, Hong Kong, China and Department of Economics, 
	Lingnan University, Hong Kong, China.

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.
Title: Big data, computational science, economics, finance, marketing, management, and psychology: connections
Abstract: The paper provides a review of the literature that connects Big Data, Computational Science, Economics, Finance, Marketing, Management, and 
	Psychology, and discusses some research that is related to the seven disciplines. Academics could develop theoretical models and subsequent 
	econometric and statistical models to estimate the parameters in the associated models, as well as conduct simulation to examine whether the estimators 
	in their theories on estimation and hypothesis testing have good size and high power. Thereafter, academics and practitioners could apply theory to 
	analyse some interesting issues in the seven disciplines and cognate areas.
Classification-JEL: A10, G00, G31, O32.
Keywords: Big Data, Computational science, Economics, Finance, Management, Theoretical models, Econometric and statistical models, Applications.
Length: 56 pages 
Creation-Date: 2018-01
Number: 2018-05
X-File-Ref: http://america.sim.ucm.es/repec/ucm/ref/doicae1805.txt
File-URL: https://eprints.ucm.es/id/eprint/46293/1/1805.pdf
File-Format: Application/pdf
Handle: RePEc:ucm:doicae:1805