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Time Series and Panel Data for the Social Sciences

UW CSSS/POLS 512

A survey of regression models for time series and time series cross-sectional data. Emphasis on modeling dynamics and panel structures with continuous outcomes, as well as on interpretation and fitting of models. Topics vary and may include trends and seasonality, ARIMA models, lagged dependent variables, distributed lags, cointegration and error correction models, fixed and random effects, panel heteroskedasticity, missing data imputation, and causal inference using panel data.



UW CSSS/POLS 512

Time Series and Panel Data for the Social Sciences

Offered occasionally at the
University of Washington


For my Essex Summer School
     course, click Panel Data again.


Syllabus  

Readings  



Spring 2022

Class meets:
TTh 4:30–5:50 pm
Class is taught in person at Smith 405

TA:

Tao Lin
(UW Political Science)

Section meets:
F 1:30–3:30 pm
Section taught over Zoom

Lectures           Click on lecture titles to view slides or the buttons to download them as PDFs.

Topic 1

Course Introduction / Review of Linear Regression and Simulation    

Students should also review Topics 1 and 2 from my maximum likelihood course; I will answer questions about these lecture slides during the first week. Students looking to brush up on matrix algebra may want to read through Kevin Quinn’s matrix algebra review. Finally, in conjunction with these slides I discuss examples that can be found in slides on labor standards in Africa and intimate partner homicide in the US.


Topic 2

Basic Concepts for Time Series: Trends, Lags, and Cycles    

The univariate time series simulation function for R mentioned in the lecture is available here; this function allows for deterministic trends, stationary and nonstationary ARMA processes, and additive or multiplicative seasonality. Also available is a simpler but less flexible R script showing simulation and diagnostics of stationary processes using built-in functions.


Topic 3

Modeling Stationary Time Series    

Example code and csv data for estimating and interpreting ARMA models in R.


Topic 4

Modeling Nonstationary Time Series    

Example code for estimating and interpreting ARIMA and ECM models in R. You will also need this helper function for plotting counterfactual time series using R base graphics.


Topic 5

Basic Concepts for Panel Data    

For the curious, the R script used to construct the example plots in the first half of this lecture is here.


Topic 6

Panel Data Models with Many Time Periods    

Panel ARIMA and fixed effects models in R: example code and csv data for estimating and interpreting panel models with a large number of periods and a small number of cross-sectional units.


Topic 7

Panel Data Models with Few Time Periods    

Panel GMM models (Arellano-Bond/Blundell-Bond) in R: example code, helper functions, and csv data for estimating and interpreting panel models with a small number of periods and a large number of cross-sectional units. On Nickell bias: a script to plot the asymptotic results of Nickell (1981) as well as a helper file and two Monte Carlo scripts (large N / large β and large N / small β) to produce the finite sample results in the lecture slides.


Topic 8

Heteroskedasticity in Panel Data    


Topic 9

In-Sample Simulation for Panel Data Models  

Example code for simulating in-sample unit-by-unit from a panel data model. Uses the cigarette data and helper functions from Topic 7.


Advanced Topic 1

Missing Data and Multiple Imputation for Panel Data    


Advanced Topic 2

Panel Data with Binary Dependent Variables    


Student Assignments

Problem Set 1  

due via Canvas on Tuesday 19 April 2022

Data for problem 1. Data for problem 2. Data for problem 3.


Problem Set 2  

due via Canvas on Tuesday 10 May 2022

Data for problems 1 and 2.


Problem Set 3  

due via Canvas on Tuesday 24 May 2022

Data for problem 1.


Poster Presentations

31 May 2022 to 2 June 2022

Requirements and suggestions for poster presentations will be discussed in class.


Final Paper

Due Tuesday 7 June 2022 at noon by email

See the syllabus for paper requirements, and see my guidelines and recommendations for quantitative research papers.


Labs

Lab 1

Working with Time Series and Panel Data in R

Lab 1 slides, replication files and lab section recording.


Lab 2

Linear Regression and Time Series Diagnostics

Lab 2 slides, replication files and lab section recording.


Lab 3

Time Series Diagnostics

Lab 3 slides, replication files and lab section recording.


Lab 4

Time Series Model Estimation and Assessment

Lab 4 slides, replication files and lab section recording.


Lab 5

Non-stationary Time Series

Lab 5 slides, replication files and lab section recording.


Lab 6

Fixed Effect, Random Effect, and Dynamic Structure in Panel Data Model

Lab 6 slides, replication files and lab section recording.


Lab 7

Dynamic Panel Model

Lab 7 slides, replication files and lab section recording.


Lab 8

In-sample Simulation for Panel Data Model

Lab 7 lab section recording.



University of Washington link

CSSS Center for Statistics and the Social Sciences link

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