Course Description
Advanced topics in time series analysis and forecasting. Linear and nonlinear time series will be discussed. Topics include stationary processes, autocorrelation functions, various univariate time series models, forecasting, and multivariate time series. The focus is mostly on theoretical topics, but some applications are covered.
Athena Title
TIME SERIES
Prerequisite
STAT 6820 or STAT 8260
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Course Objectives
The objective is to provide a foundation in core time series methods that will permit students to undertake serious empirical work or pursue more advanced modeling for time series data. Students will be expected to have an understanding of various models suitable for analyzing time series data, how to fit such models and use them to forecast future observations.
Topical Outline
1. Basics of Time Series Analysis: stationarity, trend, seasonal components, autocorrelation, prediction. 2. Linear time series models: linear regression in time series and ARMA models. 3. Nonlinear time series models. 4. Models of volatility: ARCH and GARCH models. 5. Overview of nonparametric smoothing methods: kernels and splines. 6. Smoothing in time series analysis. 7. Multivariate time series analysis: VAR models. 8. Additional topics at the discretion of the instructor.