Econometric Methods for Big Data

ECON-4280

Examine advanced econometric and statistical methods for the analysis of high-dimensional data, otherwise known as "Big Data." In this setting, detailed information for each unit of observation informs machine learning techniques such as classification and regression trees; random forests; penalized regressions; and boosted estimation. These prediction methods are then utilized to improve causal modeling, with applications in the study of healthcare demand and supply modeling, and behavior of consumers and businesses.

4 credits

Prereqs:

one of:

ECON-1200 Introductory Economics IHSS-1200 Principles of Economics

and

one of:

MATH-1010 Calculus I MATH-1500 Calculus for Architecture, Management, and HASS

and

one of:

ENGR-2600 Modeling and Analysis of Uncertainty MGMT-2100 Statistical Methods MATP-4600 Probability Theory and Applications PSYC-2310 Research Methods and Statistics I

Past Term Data

Offered

Not Offered

Offered as Cross-Listing Only

No Term Data

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	Spring	Summer		Fall
		(Session 1)	(Session 2)
2023	Econometric Methods-big Data (4c) Huaming Peng Seats Taken: 7/19			Econometric Methods-big Data (4c) Huaming Peng Seats Taken: 0/19
2022	Econometric Methods-big Data (4c) Jason Ung Huh Seats Taken: 9/19			Econometric Methods-big Data (4c) Huaming Peng Seats Taken: 12/19
2021	Econometric Methods-big Data (4c) Jason Ung Huh Seats Taken: 11/19			Econometric Methods-big Data (4c) Huaming Peng Seats Taken: 11/19
2020				Econometric Methods-big Data (4c) Huaming Peng Seats Taken: 10/19
2019				Econometric Methods-big Data (4c) Huaming Peng Seats Taken: 6/19
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