{ "cells": [ { "cell_type": "markdown", "id": "b7981852", "metadata": {}, "source": [ "# Course Syllabus" ] }, { "cell_type": "code", "execution_count": 1, "id": "fa0a91ab", "metadata": { "tags": [ "remove_input", "remove_output" ] }, "outputs": [ { "data": { "text/plain": [ "'252 Chancellors Hall'" ] }, "metadata": { "scrapbook": { "mime_prefix": "", "name": "officeg" } }, "output_type": "display_data" }, { "data": { "text/plain": [ "'Fall 2023'" ] }, "metadata": { "scrapbook": { "mime_prefix": "", "name": "termg" } }, "output_type": "display_data" }, { "data": { "text/plain": [ "'rob.hicks@wm.edu'" ] }, "metadata": { "scrapbook": { "mime_prefix": "", "name": "emailg" } }, "output_type": "display_data" }, { "data": { "text/plain": [ "'TR 5:00-6:20 pm'" ] }, "metadata": { "scrapbook": { "mime_prefix": "", "name": "classtimeg" } }, "output_type": "display_data" }, { "data": { "text/plain": [ "'Wednesday 4:00 - 5:00pm'" ] }, "metadata": { "scrapbook": { "mime_prefix": "", "name": "officehoursg" } }, "output_type": "display_data" }, { "data": { "text/plain": [ "'113 Chancellors Hall'" ] }, "metadata": { "scrapbook": { "mime_prefix": "", "name": "classroomg" } }, "output_type": "display_data" }, { "data": { "text/plain": [ "'Oct. 10'" ] }, "metadata": { "scrapbook": { "mime_prefix": "", "name": "midtermg" } }, "output_type": "display_data" } ], "source": [ "# set variables for this semester\n", "office = '252 Chancellors Hall'\t\t\n", "term = 'Fall 2023'\t\t\t\t\t\t\n", "email = 'rob.hicks@wm.edu'\t\t\t\n", "classtime = 'TR 5:00-6:20 pm'\t\n", "officehours = 'Wednesday 4:00 - 5:00pm'\n", "classroom = '113 Chancellors Hall'\n", "# coffeehour = '4:00-5:00pm Sunday'\n", "midterm = 'Oct. 10'\n", "\n", "# set dates for this semester\n", "dates =\t [['First day of class',\t 'Aug. 31'], \n", "\t\t ['Mid-Term',\t\t\t midterm], \n", "\t\t ['Fall Break',\t\t\t 'Oct 12-15'], \n", " ['Thanksgiving',\t\t\t 'Nov. 22-26'], \n", "\t\t ['Last day of this class', 'Dec. 7'], \n", "\t\t ['Final Exam',\t\t\t 'Dec. 14 (7:00 - 10:00pm)']] \n", "\n", "from myst_nb import glue\n", "# put all data for this semester here\n", "glue(\"officeg\", office)\n", "glue(\"termg\", term)\n", "glue(\"emailg\", email)\n", "glue(\"classtimeg\", classtime)\n", "glue(\"officehoursg\", officehours)\n", "glue(\"classroomg\", classroom)\n", "# glue(\"coffeehourg\", coffeehour)\n", "glue(\"midtermg\", midterm)\n", "\n", "import pandas as pd\n", "df = pd.DataFrame(dates, columns=['Item', 'Date'])" ] }, { "cell_type": "markdown", "id": "863d02e5", "metadata": {}, "source": [ "- **Instructor**: Rob Hicks\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n", "- **Office**: {glue:text}`officeg`\n", "- **Term**: {glue:text}`termg`\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n", "- **E-mail**: {glue:text}`emailg`\n", "- **Class Time**: {glue:text}`classtimeg` \n", "- **Class Room**: {glue:text}`classroomg`\n", "\n", "## Course Summary\n", "\n", "In this course we will explore econometric techniques for testing\n", "microeconomic theories at the individual or firm level. With the advent\n", "of computers and associated data on economic behavior, the past few\n", "decades has seen an explosion of applied economic research using a wide\n", "range of techniques for this type of data- termed cross section data and\n", "cross section econometrics. In this course, you will learn about these\n", "techniques, will learn to be an educated consumer of econometric\n", "research, and will apply these techniques to real data. We will also\n", "derive many of the properties of the statistical techniques used in this\n", "course, but primarily at the end of the course you will\n", "\n", "- Understand the strengths and weaknesses of cross section techniques\n", "- Know how to test the validity of modeling assumptions\n", "- Know the proper econometric technique for a wide variety of\n", " cross section settings\n", "\n", "### Topics\n", "\n", "We will be covering the following topics this semester. Each of these\n", "have an entry at the course's [google\n", "classroom](https://classroom.google.com/u/1/c/NTI2NDQxNzU5NDc3):\n", "\n", "- Introduction to the Course\n", "- Ordinary Least Squares\n", "- Endogeneity and IV Regression\n", "- Panel Data\n", "- Maximum Likelihood Estimation\n", " - Probit and Logit models\n", " - Truncation, Censoring, and Sample Selection\n", "\n", "If time allows we might cover additional topics.\n", "\n", "## Important Dates\n", "\n", "
" ] }, { "cell_type": "code", "execution_count": 2, "id": "7c149d83", "metadata": { "tags": [ "remove_input" ] }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ItemDate
First day of classAug. 31
Mid-TermOct. 10
Fall BreakOct 12-15
ThanksgivingNov. 22-26
Last day of this classDec. 7
Final ExamDec. 14 (7:00 - 10:00pm)
" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import HTML\n", "def make_lalign_formatter(df, cols):\n", " return {col: '{{:<{}s}}'.format(df[col].str.len().max()).format for col in cols}\n", "\n", "HTML(df.to_html(index=False, justify='left'))" ] }, { "cell_type": "markdown", "id": "615d4c90", "metadata": {}, "source": [ "
\n", "\n", "## Course Materials\n", "\n", "All course materials are available online either here or at the\n", "course's google clasroom. I will **only** be using blackboard for\n", "posting grades and problem set solutions.\n", " \n", "- [Syllabus (this document)](https://econ.pages.code.wm.edu/407/syllabus/docs/index.html)\n", "- [Course Notes](https://econ.pages.code.wm.edu/407/notes/docs/index.html)\n", "- Presentations, handwritten notes from class, and code at the [Course\n", " Google Classroom Site](https://classroom.google.com/u/0/w/NTI2NDQxNzU5NDc3/t/all)\n", "- Data for Stata exercises found at \n", "\n", "## Logistics\n", "\n", "- **Office Hours**: {glue:text}`officehoursg` or by appointment. \n", "\n", "- **Asking Questions**\n", "\n", " Substantive questions about course material or coding in Stata\n", " must be submitted to the Cross Section issue tracker at\n", " . Part of Problem\n", " Set 1 requires you to file an issue at this site, **so make sure\n", " you can log in ASAP**. Do not post proofs or code on the issue\n", " tracker that effectively answers an exam or problem set\n", " question. Use email for these types of questions.\n", "\n", "- **Email Policy** \n", "\n", " For other types of questions like setting up meetings, grade\n", " questions, logistical issues, etc., I will respond to emails but\n", " only if they contain the tag `[ECON407]` in the subject line. If\n", " not, the google will likely delete your email. Substantive\n", " questions about course material/coding should be posted to the\n", " issue tracker as described in the previous bullet point unless you\n", " feel that the code or information you provide in the issue tracker\n", " will answer the question.\n", "\n", "- **Course Teaching Assistant**\n", "\n", " This semester I will have a teaching assistant who will work with\n", " students on technical issues related to `jupyter lab` and code\n", " syntax. The teaching assistant will not answer any substantive\n", " questions about class content. You will receive more information\n", " on the first day of class.\n", "\n", "- **Thanksgiving Week**\n", "\n", " As instructed by the Dean, the Monday lecture for the week of\n", " Thanksgiving will not require you to be on campus. Instead, you\n", " will be watching a recorded lecture with content to be determined\n", " as the date nears.\n", "\n", "- **Grades** \n", "\n", " Your grade will be based on five exercises (1 @ 5% and 4 @ 10% each),\n", " a mid-term (25%), a final exam (30%).\n", " -\tThe **problem sets** will consist of a mix of theoretical and\n", " practical econometrics and should be considered serious\n", " time-consuming undertakings. In each (after the first one),\n", " you will be given a dataset and will need to conduct an\n", " econometric analysis thinking critically about which\n", " technique to employ as well as key tests that should be\n", " run. Your document should include clear interpretations of\n", " your results, tables with clear variable names, and be\n", " well-formatted with code, tables, and writeup combined in a\n", " convenient way for showcasing your work using a `jupyter\n", " notebook`. You will be responsible for posting to the\n", " blackboard assignment a `jupyter notebook` file that\n", " **completely** generates your analysis: a flowing narrative\n", " containing code, writeup, and results. For any part of the\n", " problem sets requiring hand-written math excercises, this\n", " content must be incorporated in the `jupyter notebook`.\n", " Early on in class, I will demo how `jupyter` works with\n", " `stata` and how to include handwritten work in a jupyter\n", " notebook file. I will not accept problem set responses that\n", " are word documents, pdf's, png images, etc. It must be a\n", " `jupyter notebook`.\n", " \n", " -\tThe **mid-term** is scheduled for {glue:text}`midtermg`.\n", " The final exam time is fixed and can\\'t be rescheduled\n", " according to university policy. If these exam times don\\'t\n", " work for you please drop the course.\n", " \n", " -\tGrades will be awarded based on standard grading scales\n", " **after** a curve is applied (if necessary):\n", " \n", " | Letter | **+** | | **-** |\n", " |----------|-----------|----------|-----------|\n", " | A | N/A | 93-100% | 90-92% |\n", " | B | 87-89% | 83-86% | 80-82% |\n", " | C | 77-79% | 73-76% | 70-72% |\n", " | D | 67-69% | 63-66% | 60-62% |\n", " | F | N/A | 0-59% | N/A |\n", " \n", " For a typical class year, the points required for an \\\"A\\\" in\n", " the course **before the curve is applied** is approximately\n", " 84-85% of the total available points. \n", "\n", "- **Policy on Late Assignments**\n", "\n", " -\tFinal Exam: University policy will not allow me to reschedule the\n", " final exam ([see the Dean of Students for\n", " exceptions](https://www.wm.edu/offices/registrar/calendarsandexams/examschedules/rescheduledeferexam/)).\n", " \n", " -\tCourse assignments must be turned in on time. Late work will be\n", " accepted for up to two additional days (with Saturday and Sunday\n", " counting as 1 day in total) with a letter grade deduction for each\n", " late day. After two days, late assignments will not be accepted. See\n", " below for some examples:\n", " \n", " | Due Date | Turned in | Your Grade | Your Grade after Penalty |\n", " |:--------:|:------------------:|:----------:|:------------------------:|\n", " | Tuesday | Thursday | A | C |\n", " | Thursday | Saturday or Sunday | A | C |\n", " | Tuesday | Friday | A | F (not accepted) |\n", " | Thursday | Monday | A | F (not accepted) |\n", " \n", "- **Turning in your work**\n", "\n", " All assignments **must** be submitted via the appropriate\n", " assignment in blackboard **by the time stipulated in the\n", " blackboard assignment item**.\n", " \n", " - **Problem sets** require a `jupyter notebook`\n", " \n", " 1.\tthat produces your document. If this file fails to\n", " \taccurately produce the pdf of your dynamic document, there\n", " \twill be an automatic two letter grade reduction.\n", " 2.\tthat contains additional materials such as handwritten\n", " \tequations for written work\n", " 3. you must organize your work using the notebook template I\n", " will distribute to you as the Problem Set.\n", " \n", " - **Exams** will be administered in class and will be handwritten\n", " not requiring computers.\n", "\n", "- **Grade Discrepancies and Grade Questions**\n", "\n", " I am happy to discuss questions you have about your grade on class\n", " assignments. Any questions you have regarding a potential grade change\n", " on an assignment must be cleared up within 1 week of receiving your work\n", " back from me. The only exception to this policy is if I made an\n", " arithmetic or data entry error in adding your score up and entering it\n", " into blackboard. *I will not entertain grade questions at the beginning\n", " of or following a class*. These need to be handled in my office.\n", "\n", "- **Computing, Computers, and the Class**\n", "\n", " We will make extensive use of `Stata` or `R` inside a `jupyter\n", " notebook`. For running either `Stata` or `R`, you will use\n", " [https://jupyterhub.wm.edu](https://jupyterhub.wm.edu). Due to\n", " licensing restrictions there are only 15 available seats on\n", " `jupyterhub`, therefore start assignments early. To avoid\n", " capacity constraints on `jupyterhub`, you may want to buy your\n", " own copy of Stata (using the Stata *Grad Plan* at\n", " ) and the cost is around \\$100. You may\n", " need to contact me for some small setup steps to get `jupyter\n", " notebook` running on your own PC/Mac if you go this route.\n", " Please get things working on your computers ASAP.\n", " \n", " I will make some use of the main computer in the classroom for\n", " much of what we do together in class. The data for course\n", " examples and problem sets will be available on the web for the\n", " duration of the course. \n", " \n", "- **Acceptable Collaboration and Automatic Plagiarism Scanning**\n", " \n", " Finally, I want to define acceptable collaboration. In this\n", " course, I want you to think for yourself in applying these\n", " techniques. Students have commented that struggling on these\n", " problem sets helps in preparing for exams. \n", " \n", " Judicously using some resources from the web and properly citing\n", " it, is perfectly with-in bounds. Copying `.do`, `.ipynb`, or\n", " types of work from students in this class or off of the web and\n", " treating them as your own work is not acceptable. Receiving\n", " assistance at every critical modeling step is also not\n", " acceptable. Asking a classmate about clarification of stata\n", " syntax (e.g.- I forgot how to ask for robust standard errors,\n", " could you help me with that?), is fine.\n", " \n", " Be aware that all assignments submitted to blackboard are\n", " automatically scanned by `Turnitin` and are compared against the\n", " assignments for all current and past Cross Section classes as\n", " well against information from the web.\n", "\n", "### Additional Books\n", "\n", "I am no longer requiring any books for this class. However, there are\n", "two I highly recommend particularly if you plan on attending graduate\n", "school in Economics or Political Science. First is Greene\\'s\n", "[Econometric\n", "Analysis](https://www.amazon.com/Econometric-Analysis-8th-William-Greene/dp/0134461363/ref=sr_1_1?ie=UTF8&qid=1503743002&sr=8-1&keywords=green+econometrics).\n", "Second is [this\n", "one](https://www.amazon.com/Econometric-Analysis-Cross-Section-Panel/dp/0262232588/ref=sr_1_2?ie=UTF8&qid=1503743061&sr=8-2&keywords=wooldridge+econometrics)\n", "by Wooldridge which is recommended but not required. Both of these books\n", "are expensive and you can find older editions at less than half the\n", "price. \n", "\n", "For your convenience, the table below lists topics we may cover with\n", "links to the relevant parts of the two books by Green and Wooldrige.\n", "The order of topics in the reading list below does not necessarily\n", "reflect the order topics will be covered in class.\n", " \n", " | Topic | Summary & Notes | Chapters |\n", " |------------------------------|-------------------------------|----------------------|\n", " | Introduction | Linear Algebra Intro/ Review | Green Appendix |\n", " | | Stata Intro/Review | Supplemental Notes |\n", " | | Review of Regression | Greene 3,4 |\n", " | | Review of Endogeneity | Green 12,13.5,13.5.5 |\n", " |------------------------------|-------------------------------|----------------------|\n", " | Panel Data | Fixed Effects | Green 9-9.7 |\n", " | | Random Effects | Wooldridge 10 |\n", " |------------------------------|-------------------------------|----------------------|\n", " | Maximum Likelihood | Intro to Maximum Likelihood | Green 16 |\n", " |------------------------------|-------------------------------|----------------------|\n", " | Discrete Dependent Variables | Binomial Logit and Probit | Green 23 |\n", " |------------------------------|-------------------------------|----------------------|\n", " | Truncation & Censoring | Tobit and Heckman | Green 24 |\n", " |------------------------------|-------------------------------|----------------------|\n", " | Simulation and Bootstrap | Simulating standard errors | TBA |\n", " |------------------------------|-------------------------------|----------------------|\n", " | Other MLE models | Multinomial Logit and Probit | |\n", " | | Negative Binomial and Poisson | Green 25 |\n", " |------------------------------|-------------------------------|----------------------|" ] } ], "metadata": { "jupytext": { "text_representation": { "extension": ".md", "format_name": "myst", "format_version": 0.13, "jupytext_version": "1.10.3" } }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.17" }, "source_map": [ 12, 16, 49, 97, 105 ] }, "nbformat": 4, "nbformat_minor": 5 }