Time Series Analysis

Instructor(s):

  • Sara Mitchell, University of Iowa
  • Clayton Webb, Texas A&M University

Many of the data sets that social scientists analyze are organized over time, including leader approval, GDP per capita, homicide rates, and political violence. While many of the tools that students learn in regression courses are useful for analyzing time series data, there are several unique properties of time series data that must be understood before working with such data. This course provides an introduction to methods of time series analysis, building upon students' background knowledge in statistical inference and regression analysis. We will begin with basic descriptive methods for viewing time series data and then talk about stationarity assumptions and how violations of these assumptions threaten inferences in regression analyses of time series data. Students will also learn about autoregressive integrated moving average (ARIMA) models, including autocorrelation (ACF) and partial autocorrelation (PACF) functions. We will review several statistical tests for unit roots, serial correlation, and normality. Students will be introduced to regression-based time series models, such as the autoregressive distributed lag (ADL) model. We will also learn about modeling interventions in time series data. In addition to learning about tests and models for single time series, the course will introduce students to pooled time series models including panel unit root tests and models that capture fixed or random effects. We will also introduce models for long memory / fractionally integrated processes (ARFIMA), conditional volatility (ARCH-GARCH), and multivariate time series methods (VAR).

Prerequisites: Participants must be familiar with basic regression analysis and the fundamentals of statistical inference. The instructors will provide time series datasets that you can use for the assignments.

Software: The course will use Stata 13 and R.

Reading Materials: Box-Steffensmeier, Janet M., John R. Freeman, Matthew P. Hitt, Jon C. W. Pevehouse. 2014. Time Series Analysis for the Social Sciences. New York: Cambridge University Press.

Fees: Consult the fee structure.

Tags: time series, autoregressive, intervention analysis, error correction model, stationary data, non-stationary data, unit root

Course Sections

Section 1

Location: ICPSR -- Ann Arbor, MI

Date(s): June 22 - July 17

Time: 1:00 PM - 3:00 PM

Instructor(s):

  • Sara Mitchell, University of Iowa
  • Clayton Webb, Texas A&M University

Syllabus:

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