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 Tuesday 19 April 2022 Data for problem 1. Data for problem 2. Data for problem 3. due via Canvas on Tuesday 10 May 2022 Data for problems 1 and 2. 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. |

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