Lectures PDFs of slides are best viewed in Adobe Acrobat, rather than in your browser. 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 in pdf format. 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 by email Tuesday 21 April 2020 Data for problem 1. Data for problem 2. Data for problem 3. due by email Tuesday 12 May 2020 Data for problems 1 and 2. due by email Tuesday 26 May 2020 Data for problem 1. Poster Presentations 28 May 2020 to 4 June 2020 Requirements and suggestions for poster presentations will be discussed in class. Final Paper Due Tuesday 9 June 2020 at noon by email See the syllabus for paper requirements, and see my guidelines and recommendations for quantitative research papers. Labs Lab 1 April 3rd, 2020, PST 1:30pm Logistics & R Refresher Lab 2 April 10th, 2020, PST 1:30pm Working with Time Series and Panel Data in R + (P)ACF Lab 2 slides, code, and data. For RMarkdown ver., click here. Lab 3 April 24th, 2020, PST 1:30pm Studying & Modeling (Unknown) Time Series in R Lab 4 May 8th, 2020, PST 1:30pm Counterfactual Forecasting with Time-Series and Panel Data in R Lab 5 May 15th, 2020, PST 1:30pm Panel Data Models with Many Time Periods (Variable Intercept) in R Lab 6 May 22th, 2020, PST 1:30pm Panel Data Models with Few Time Periods (Dynamic Panel Model) in R Lab 7 May 29th, 2020, PST 1:30pm In-Sample Simulation for Panel Data Models in R & Presentation Preparation Lab 7 slides, code, and data. Need a helperCigs.R file in the above list. Lab 8 June 5th, 2020, PST 1:30pm An Example of Using Amelia (Multiple Imputation) in R & Extended Office Hours Lab 8 will execute a simple Amelia code with an example dataset, and hold extended office hours for problem set & data project. No slides and codes needed for Lab 8. |

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