2017-2018 Course Schedule
Fall I 2017: August 28-October 22
ADEC 7310 Data Analysis:
This course is designed to introduce students to the concepts and data-based tools of statistical analysis commonly employed in Applied Economics. In addition to learning the basics of statistical and data analysis, students will learn to use the statistical software package R to conduct various empirical analyses. The focus will be on learning to do
statistical analysis, not just on learning statistics.
Fall II 2017: October 23-December 20
ADEC 7320 Econometrics:
This course develops the foundations of predictive data analytics by introducing the key concepts of applied econometrics, the application of statistical tools used to estimate economic relationships. The main topics covered in this course include: simple and multiple linear regression, variable selection and shrinkage methods, binary logistic regression, count regression, weighted least squares, robust regression, generalized least squares, multinomial logistic regression, generalized linear models, and panel regression. The course is heavily weighted towards practical application using the R statistical programming language and data sets containing missing values and outliers. The course also addresses issues of exploratory data analysis, data preparation, model development, model validation, and model deployment.
Spring I 2018: January 16-March 11
ACED 7430 Big Data Econometrics: This course demonstrates how to merge economic data analysis and applied econometric tools with the most common machine learning techniques, as the rapid advancement of computational methods provides unprecedented opportunities for understanding “big data”. This course will provide a hands-on experience with the terminology, technology and methodologies behind machine learning with economic applications in marketing, finance, healthcare and other areas. The main topics covered in this course include: advanced regression and classification methods, resampling methods, model selection and regularization, tree-based methods, support vector machines and kernel methods, principal components analysis, and clustering methods. Students will apply supervised and unsupervised machine learning techniques to solve various economics-related problems with real-world data sets. No prior experience with R or Python is necessary.
Spring II 2018: March 12-May 15
ADEC 7460 Predictive Analytics/Forecasting: This course will expose students to the most popular forecasting techniques used in industry. We will cover time series data manipulation and feature creation, including working with transactional and hierarchical time series data as well as methods of evaluating forecasting models. We will cover basic univariate Smoothing and Decomposition methods of forecasting including Moving Averages, ARIMA, Holt-Winters, Unobserved Components Models and various filtering methods (Hodrick-Prescott, Kalman Filter). Time permitting, we will also extend our models to multivariate modeling options such as Vector Autoregressive Models (VAR). We will also discuss forecasting with hierarchical data and the unique challenges that hierarchical reconciliation creates. The course will use the R programming language though no prior experience with R is required.
➢ Prerequisite: College level statistics