Time Series, Panel Data & Forecasting · Quantitative Methods In The Social Sciences
Time Series analysis methods and forecasting methods
This course will introduce students to the main concepts and methods behind regression analysis of temporal processes and highlight the benefits and limitations of using temporally ordered data. Students study the complementary areas of time series data and longitudinal (or panel) data. There are no formal prerequisites for the course, but a solid understanding of the mechanics and interpretation of OLS regression will be assumed (we will briefly review it at the beginning of the course). Topics to be covered include regression with panel data, probit and logit regression of pooled cross-sectional data, difference-in-difference models, time series regression, dynamic causal effects, vector autoregressions, cointegration, and GARCH models. Statistical computing will be carried out in R.
Time series forecasting, panel data analysis, applications in social sciences.
I learned about panel data, difference-in-difference models, first difference, fixed/random effects, time series, ARIMA, forecasting, and a couple of machine learning applications.
Great base for lots of time series techniques.
Time series model key ones: AR, arima, fixed effect, random effect, first difference
Methods (specifically different types of regression, diagnostic tests, etc) that can be applied when working with panel data and time series and/or attempting to forecast
A whole lot of time series and panel data regression models. The panel data models included OLS, GLS, First Differences, Fixed Effects, Random Effects. For time series we discussed unit roots and serial correlation as well as forecasting and ARIMA models.
Methods for working with panel data and time series data
This is a great course for learning about time series and several different statistical methods that incorporate time, which is useful in a variety of different contexts. If you have any interest in the subject matter, or even have an interest in further economics education, this is a great course. I do think this course would be best taken after Data Analysis (or for students who have previously completed an introductory econometrics course), but is only offered in the Fall semester unfortunately.