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 Temporal Concepts: Trends, Stochastic Processes, and Seasonality 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 in class Tuesday 21 April 2015 Data for problem 2. Data for problem 3. due in class Thursday 30 April 2015 Data for problem 1. due in class Tuesday 12 May 2015 Annual data and quarterly data for problem 1. Poster Presentations 21 May 2015 to 2 June 2015 Requirements and suggestions for poster presentations will be discussed in class. Final Paper Due Tuesday 9 June 2015, 3:00 pm, both in my Gowen mailbox and by email See the syllabus for paper requirements, and see my guidelines and recommendations for quantitative research papers. Labs Lab 1 Review of Linear Regression and R Programming Lab 1 code, and data for Robey and More, and Iversen and Soskice. Lab 2 Temporal Concepts: Trends, Stochastic Processes, and Seasonality Lab 2 code, and data for Accidental Deaths in the US. Lab 3 Modeling Stationary Time Series Lab 3 code, and data for UK Vehicle Accident Deaths. Lab 4 Modeling Nonstationary Time Series Lab 4 code, and data for US unemployment, and crude oil prices. Lab 5 Panel Data Models with Variable Intercepts Lab 5 code, and data for Przeworski et al. Lab 6 Dynamic Panel Data Models Lab 6 code, and data for cigarette consumption. Lab 7 In Sample Simulation for Panel Data Models Lab 7 code. |

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