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. 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.
This course runs January 8-12, 2018.
TEACHING FELLOW: Rodrigo Nakahara, University of São Paulo
Day 1: The first day is designed to review standard regression using Ordinary Least Squares (OLS).
Day 2: On the second day, we extend our previous discussion to cover Generalized Least Squares (GLS). We will cover statistical and data issues (heteroscedasticity, multicollinearity, autocorrelation), and how to diagnose and address them.
Day 3: On the third day, we dive into time series analysis. 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.
Day 4: The fourth day, we will build off previous days to add a model important to univariate time series, ARIMA models. We will cover how to estimate these models, test for statistical issues, and make forecasts using our models.
Day 5: The last day, we will have student presentations of your research project you have developed over the week. Everyone will provide feedback. If needed, we will also finish up any lectures.