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 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 due via Canvas on Monday 15 April 2024 Data for problem 1. Data for problem 2. Data for problem 3. due via Canvas on Monday 6 May 2024 Data for problems 1 and 2. due via Canvas on Monday 20 May 2024 Data for problem 1. Poster Presentations 22 to 29 May 2024 Requirements and suggestions for poster presentations will be discussed in class. Final Paper Due Tuesday 4 June 2024 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 Supplementary material: You can access all the lab materials in the following .zip file, and to the lab session recording. Lab 2 Supplementary material: After reviewing concepts, we will go voer the following .rmd file file which covers the necessary functions for time series diagnostics. Find in this .r file the code solutions for the practice exercise of identifying time trends. For the exercise, you will need this dataset, and to run the .rmd file this, and this datasets. You can access all the lab materials in the following .zip file, and to the lab session recording. Lab 3 Modeling Stationary Time Series Supplementary material: After the presentation preview, we will go over the following .rmd file file, with all the lab contents for today. You will need the following dataset for the rmd file. This lab also largley replicates Chris's code for this module. You can access all the lab materials in the following .zip file, and to the lab session recording. Lab 4 Modeling Non-Stationary Time Series Supplementary material: After the presentation preview, we will go over the following .rmd file file, with all the lab contents for today. You will need the following dataset for the rmd file. You can access all the lab materials in the following .zip file, and to the lab session recording. Lab 5 Fixed and Random Effects in Panel Data Analysis Supplementary material: After the presentation preview, we will go over the following .rmd file file, with all the lab contents for today. You will need the following dataset for the rmd file. You can access all the lab materials in the following .zip file, and to the lab session recording. Lab 6 Nickell Bias and GMM Dynamic Panel Estimators Supplementary material: After the presentation preview, we will go over the following .rmd file file, with all the lab contents for today. You will need the following dataset for the rmd file. You can access all the lab materials in the following .zip file, and to the lab session recording. Lab 7 Dynamic Panel - In-Sample Simulation Supplementary material: This is the last lab session, which is largely a review of lab 6 and looks at Chris's code for topic 9 on in-sample simulation. You can find the .rmd file file. You will need the following dataset for the rmd file. You can access all the lab materials in the following .zip file. |

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