ABSTRACT
This chapter describes some of the most commonly used time series techniques in political communication research. The chapter considers the strengths and limitations of time series mode of analysis and compares it to other empirical approaches. It summarizes the most commonly used techniques: Regression Analyses with Time Series Data, Vector Autoregression and Granger Causality, and Autoregressive Integrated Moving Average (ARIMA) modeling. The chapter closes the discussion of time series applications by noting two areas of research: fractional integration, and time series variance models, that are common in political science but have yet to find wide application in political communication research. It also discusses the kinds of knowledge time series analysis produces relative to other forms of analysis. Perhaps the most compelling research design is one that combines time series analysis with other techniques, eliminating threats to causal inference by harnessing the distinctive strengths of different methods.
