Y577: Time Series Analysis For Social Scientists
The objective of this course is to introduce students to a variety of techniques for specifying and estimating dynamic models. After introducing some of the concepts essential to dynamic modeling, the course begins by examining Box-Jenkins, or ARIMA modeling. We then turn to times series regression and consider how the properties of time-series modeling involve additional (diagnostic) challenges but also additional (substantive) benefits compared to cross-sectional analyses. We then will devote consecutive weeks considering vector autoregression, unit-roots and cointegration, time series models for heteroskedasticity, pooled cross-sectional time series, and duration/event-history analysis.
Year(s) Offered: 2018
Class time: Wednesday : 02:30P-04:30P
Instructor: Tim Hellwig - email@example.com
Other Contact(s): Chris McCann - firstname.lastname@example.org
Prerequisites: S501 or equivalent. S503 would be useful
Algebra Required?: Yes (for notation); No (for proofs); not essential (for homework)
Calculus Required?: Yes (for concepts); No (for proofs); No (for homework);
Day(s) per week offered: Once per week with occasional lab (approx 4x semester)
Substantive Orientation: Social Sciences
Statistical Orientation: observational data
Books used: Walter Enders. 2009. Applied Econometric Time Series, 3rd ed. Hoboken, NJ: Wiley. Also Box-Steffensmeier, Freeman, and Pevehouse Time Series for Social Scientists (Cambridge, in press)
Applied/Theoretical: Closer to applied end, but not at end of continuum
Software Used: R, RATS, Stata
How the software is used: Data analysis
Problem Sets: Course may include one problem set.
Data Analysis: Yes
Keywords: Time series regression; ARIMA models; cointegration; vector autoregression; time series models for heteroskedasticity; time-series cross-section models; duration models
Comments: (1) Students have the option to use any of the three software packages listed above. (2) This course is offered (roughly) about every other year.