Advanced Statistics for Policy Analysis

Professor: Moore
Units: 1.0
Elective Course
Concentration: Quantitative Methods
Prerequisite: Empirical Analysis I, Empirical Analysis II, and knowledge of R system

This course will discuss the modeling and analysis of temporal and spatial data to inform policy issues. We will use software, namely the R system, which enables the exploration of such data. Employment data over time is a frequently encountered example of a temporal data set. Geographic data is an example of a spatial data set where the location of data points and their proximity to others is studied. Our examination of time series analysis will consider frequency-domain, relating to the number repeating events occurring per unit time, and time-domain, relating to events occurring at specific points in time, methods. For the analysis of spatial series we will use clustering, the assignment of objects to groups, and spatial or geometric methods. When the student completes this course he or she will be able to identify time and spatial series and their components as well possess the ability to undertake analysis of these data to support policy decisions.