Some of the problems use R, a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. For more information and to download the software, visit The R Project for Statistical Computing site.
Assignments
Homework 1
Solve 3 out 6 problems from Lecture 1 (PDF). For empirical questions we encourage you to go beyond the analysis in the lectures to deepen your own understanding of the concepts we are covering and their implication for real economics research.
You can use any software you like. The link to data for empirical problems is available below. Write-ups should be concise and presented in the format of short sections that can be inserted into an empirical paper or research report. For example, they could look like e.g. Section 6 in Lecture 1.
I also posted the R-code that was used to generate empirical results in Lecture 1. You are allowed and encouraged to look at the code, as this might save you a lot of time, but do write your own code (blind “copy & paste” is strictly prohibited). This forces you to learn programming and is also a good way to avoid errors I may have planted in the code.
Homework 1 Data Description (PDF)
Associated Files
R-code used to do gender wage gap (R)
Solve one of problems 1, 2, or 3 from Lecture 2 (PDF) and one of problems 4 or 5 from Lecture 2. You can use any econometric/statistical software you like. The data sets are posted below. Please provide detailed write-ups: explain theoretical foundations of your analysis and provide step-by-step explanation for your empirical analysis. Think that you are writing an empirical portion of your paper and you are trying to communicate the results to your colleagues and referees. We also provide R-code below (and a link to Stata code is also mentioned): you are welcome to take a look at the code, but you are not allowed to copy & paste the code—please write your own code. You can work in groups to discuss the homework, but all the write-ups and all the coding should be individual.
Homework 2 Data Description (PDF)
Associated Files
Data file for Homework 2 (CSV - 14.1MB)
Hint
Stata code for Weak-Id robust inference is available from Christian Hansen’s website. You can take a look, but write your own code. We also provide R-code below: you are welcome to take a look at the code, but you are not allowed to copy and paste the code—please write your own code (to learn!)
Solve one of the problems 1–4 and one of the problems 5–6 from Lecture 3 (PDF). You can use any econometric/statistical software you like. In Stata you can used “reg3” or “gmm” command to implement limited information and full information estimation. In R, please see the attached code for consultation. (Please do write your own code though). The data sets are posted below. Please provide theoretical foundations of your analysis and provide step-by-step explanation for your empirical analysis. Think that you are writing an empirical portion of your paper and you are trying to communicate the results to your colleagues and referees. You can work in groups to discuss the homework, but all the write-ups and all the coding should be individual. You must list your collaborators on the homework at the top of your submitted solutions.
Associated Files
Part A (Theory)
(i) Solve one of questions 1–5 in Lecture 4 (PDF). (ii) Solve question 1 in Lecture 5.
Part B. (Empirics)
(i) Solve question 6 from Lecture 4. (ii) Solve question 2 from Lecture 5 (PDF). You may consult the R code included (but do not copy it), it relies on the GMM package. If you are using Stata, there is a GMM command there with good documentation and there is also a bs command (no kidding) also called bstrap and bootstrap.
Part C (Bonus and Make-up Problems)
Solve problem 5 in Lecture 4 or problems 3 or 4 or both in Lecture 5. For each one of the problems you solve correctly, we will increase the previous homework grades up by 2 brackets. For example, if you earned a check- on a homework question in one the previous or current homeworks, and you solve 1 bonus problem correctly, that check- gets converted to check+. If you did an okay job on 1 problem, we will bump you up one grade bracket (1 +) for trying. If you solve all 3 problems correctly, you can earn as much as 6 + increments to grades on the previous or current HWs.
Reminder: The best 3–4 performing students in the class will get prizes from Victor in the form of books! You can get either the Van der Vaart book on Asymptotic Statistics, the Imbens Rubin book on Causal Inference, or Pearl’s book on Causality.
Rules: Solutions should preferably be submitted as pdf documents prepared in latex and the code should be submitted as .zip file. Please provide detailed write-ups: explain theoretical foundations of your analysis and provide step-by-step explanation for your empirical analysis. Think that you are writing an empirical portion of your paper and you are trying to communicate the results to your colleagues and referees. You can work in groups to discuss the homework, but all the write-ups and all the coding should be individual.
Associated Files
R-code for BS Examples in Lecture 5 (R)
Solve theoretical problems 1–2 and empirical problem 3 in Lecture 6 (PDF). Unlike in the previous homework, feel free to use the R code that is provided to you, but make sure to provide additional comments to the code explaining what each major block of the code is doing. Note that you can type (help(“glm”) to get help on the command glm, etc).
Associated Files
Solve problems 1 and 2 in Lecture 7 (PDF) and 1 in Lecture 8 (PDF). Problem 2 in L8 is optional and you earn a “++” for doing the problem. You are wellcome to use the R code provided (write comments in the code explaining what each block of the code is doing), except for question 1 in L8 you need to write your own code (which should be standard any way). By completing this problem you will have mastered 90% of the skills I wanted you to learn in this class!
Data for Lecture 7 (CSV - 2.7MB)
R-code for Lecture 8 (School Example) (R)
R-code for Lecture 8 (Democracy) (R)
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