FINANCIAL STATISTICS

STAT 612

Statistical models, methods and theory for parameter estimation, Bayesian approach to parameter estimation, methods and theory for hypothesis tests, confidence sets, two-sample testing and ANOVA, categorical data analysis, linear regression.

STAT 801

Statistical theory and methodology for modern financial data. Topics include financial stochastic models based on time series and stochastic calculus, modern statistical inference, and statistical learning for financial data as well as their applications to financial problems.

STAT 611

Probability, random variables and their distributions, joint and conditional distributions, moments and inequalities, generating functions, transformations of random variables, sampling and distribution theory, convergence concepts and limit theorems for sequences of random variables.

GENBUS 317

Statistical inference and analyses based on models will be introduced and applied in a business context using a calculus-based focus. Topics covered include point estimation, confidence intervals, hypothesis testing, regression models, and time series models. Various methods for fitting a model to data will be explored, and uncertainty about parameter estimates will be quantified so decision makers have information about the quality of the estimates. Regression and time series models are commonly used in business analytics applications and will be used to analyze business data and make inferences and predictions.

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.