REGRESSION AND TIME SERIES FOR ACTUARIES

ECON 718

Applied time series methods.

ACTSCI 657

Develop a toolbox for modeling, communicating, and managing risk and uncertainty in business applications. Emphasis on the notation of probabilistic forecasting and introduces a predictive modeling framework that integrate modern machine learning methods with distribution-based regression models. Topics include heavy-tailed regression, count data regression, survival data analysis, feature engineering using neural networks and natural language processing, among others.

ECON 724

Introduction to econometric analysis of financial time series. Covers linear econometric time series models, multivariate time series, volatility estimation, and estimating and testing financial econometric models.

ACTSCI 640

Introduction to statistical learning theory and methods for analyzing and modeling risks in actuarial applications. Topics include linear and nonlinear models; diagnostics and assessment of predictive models; variable and model selection; and non-supervised learning techniques.

STAT 349

Autocorrelation; stationarity and non-stationarity; heteroscedasticity; dynamic models; auto-regressive and moving average models; identification and fitting; forecasting; seasonal adjustment; applications for financial time series, social sciences and environmental studies.