Eco 5385.701

Predictive Analytics for Economists

Summer 2014
TTh 6:00 – 8:50 pm and Sat. 12:00 – 2:50 pm
First Day of Class: Tuesday, June 3
Last Day of Class: Tuesday, July 1
251 Maguire Building

This course is a follow-up to Eco 5350 Introductory Econometrics. Statistical methods used in engineering and computer science are introduced to complement the traditional economist’s toolbox of business and economics decision-making tools.
Purposes of Course:
There are several major purposes of this course. As the result of taking this course, the
student should have an understanding of:

• The basics of supervised learning – prediction and classification
• Prediction models including multiple linear regression, artificial neural networks, regression trees, K-nearest Neighbors
• Classification models including logit/probit models, classification trees,Naïve-Bayes models
• Model validation by means of data partitioning
• Methods of unsupervised learning – exploratory data analysis, principal components, cluster analysis, association rules
• Ensemble modeling
• How to use standard Data Mining Packages including XLMINER and SPSS Modeler