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**Essentials of Time Series for Time Series Cross-Section Analyses**

Lorena Barberia, University of São Paulo and Guy D. Whitten, Texas A&M University

**COURSE DESCRIPTION**

Time series variables (e.g., presidential approval, public mood liberalism, GDP, inflation, education level) are extremely common in the social sciences. However, due to certain properties, these series cannot always be handled using standard regression approaches. This course serves as an introduction to the world of time series analysis. In this module, we will discuss the essentials of time series with a focus on preparing you for cross-sectional time series analysis. We will explore the properties of time series (e.g., non-independence of observations, moving averages, unit-roots), and introduce strategies to test and model these data.

During the first four days, the course will involve about three hours of lecture time with breaks, then lunch, then three to four hours of hands on instruction in analysis that takes place in smaller groups using Stata. On the fifth day, students will present a specific project that applies the concepts introduced in the course.

**DATES**

This course runs January 13-17, 2020.

**Teaching Fellow:**

**COURSE OUTLINE**

Topic 1: We start with a review of standard regression assumptions. We then move into the basics of time series data.

Topic 2: We will cover how to write time series notation, how to analyze time series data, and begin to discuss threats to inference common in time series data, including autoregression and non-stationarity.

Topic 3: We introduce the basics of ARIMA models, which are used for univariate time series. Although ARIMA models are not used that frequently in applied political science research, they provide some important diagnostic procedures and a set of foundational concepts.

Topic 4: We will focus on regression models common in applied analysis (e.g.,, a dependent variable and multiple independent variables). We will discuss how autocorrelation is commonly treated in these models, lag structures, and interpreting the output from dynamic regression models.

Topic 5: We will have student presentations of a research project developed over the week. Everyone will provide feedback. If needed, we will also finish up any lectures.

**PREREQUISITES**