# ECO220 - Introductory Statistics and Econometrics

#### Smith College, Fall 2017

This syllabus is preliminary and subject to change.

• Email/hangouts: shalliday+eco220@smith.edu
• Skype: sihalliday
• Office: Pierce Hall 1.07
• Office hours: T & Th 2-3:30pm
• Class schedule: T & Th 9-10:20 pm
• Venue: Seelye 308

## Course Description

Economics 220 is an introductory course in statistics for economists. Statistics is a mathematical tool for analyzing quantitative data. This course covers descriptive statistics, probability, basic inferential statistics (estimation and hypothesis testing) and regression analysis (econometrics).

The goal of the course is to help you learn how to use statistical methods to describe data and to test theories about relationships among variables and to make you an informed user of other people’s statistical analysis and evidence. The course emphasizes economic applications and interpretation and policy implications of statistical results.

The course is divided into lectures and labs. The lectures present the basic theory and methods of statistics, while the labs provide more opportunity for discussion of concepts and problems and practice in computer-based statistical analysis of (primarily) economic data.

It is important that you read the chapter assignments before attending the lectures on the assigned chapters. Even if you don’t always have time to read the entire assignment carefully, you should at least skim through the material before class to become familiar with the terms and concepts that will be discussed. This will make the lectures more meaningful and easier to follow.

For students who are interested in learning more about econometrics, I recommend taking Eco 240 Econometrics as a follow up to this class.

## Prerequisites

In order to take this class, you must have previously taken either ECO150 (Introductory Microeconomics) or ECO153 (Introductory Macroeconomics).

Important: Students will not be given credit for both ECO 220 and any of the following courses: MTH 190/PSY 190, GOV 190, MTH 241, MTH 245, or SOC 201.

I shall assume that you are comfortable with basic calculus and algebra. If you are not, then you should make sure to go to the review sessions at the Spinelli QLC. Why do you need these?

• Algebra helps you to think about the ways functions work, finding solutions by substitution, understanding the rules of logs and exponents, and making sure you understand basic graphs
• Calculus allows you to take a further step and understand slopes and rates of change, calculus is therefore especially important when you want to understand whether and to what extent variables affect or correlate with each other

## Learning Goals

I separate learning goals into goals with different verbs: know, understand, comprehend, analyze, synthesize, do, etc.

• Know the virtues and limitations of statistics and its uses in economics.
• Understand the role of statistics in understanding economic models and economic theory.
• Analyze data from experiments and surveys to answer questions relevant to economics and the behavioral sciences.
• Find ways to wrangle data and play around with computing to derive useful insights using Stata and MS Excel/Google Sheets.
• Recognize the benefits of teaching yourself to do new things.

## Course Surveys

Please make sure you complete these surveys by the end of the first week of term.

## Method of Instruction

The course is a lecture-based course with “labs” and a substantial amount of student participation and teamwork. Students are expected to prepare the chapter readings for each session and to be able to answer questions about the readings to produce a high quality discussion. Each student is expected to contribute to the discussion. If you do not contribute, I shall encourage you to do so. If you contribute substantially more than anyone else, then I may ask you rather to encourage the engagement of others. We will also employ peer evaluation, though the professor will award grades.

## Textbook

The following textbook is required for the course: Essential Statistics, Regression, and Econometrics, 1st Edition; Gary Smith; 2011; Academic Press; ISBN: 9780123822215. There is a copy of the textbook on reserve at the Neilson Library.

## Course Schedule

Below is a tentative and preliminary course schedule. It is subject to change depending on what happens during the semester (snow, random days off and such).

Date Week Topic Reading Exclusions Assignment
1/25 Week 1 Intro, Data & Visualization Ch. 1 - -
1/30-2/1 Week 2 Visualization & Summary Stats Chs. 2 and 3 ex 3.4 & 3.5 PS 1 Out Tues
2/6-8 Week 3 Risk Ch. 3 Lab 1 PS 1 due Tues
2/13-5 Week 4 Risk Ch. 3 - -
2/20-22 Week 5 Time Ch. 4 Lab 2 ER2
2/27-3/1 Week 6 Fall Break - - -
3/6-8 Week 7 Learning & Info BE Ch. 5 Lab 3 -
3/10-18 - Spring Break - - -
3/20-22 Week 8 Learning & Info BE Ch. 5 & S. 6.4 Experiment/Lab -
3/27-29 Week 9 Social Preferences BE Ch. 7 Lab 4 -
4/3-5 Week 10 Social Preferences BE Ch. 7 Lab 5 -
4/10-12 Week 11 Otelia Cromwell Day - - -
4/17-19 Week 12 Social Preferences BE Ch. 7 Lab 6 Midterm
4/24-26 Week 13 Gender Readings Experiment/Lab -
5/1-3 Week 14 Happiness & Utility BE Ch. 10 Lab 7 ER4
5/4-7 - Reading Period - - -
5/8-11 - Exam Period - - -

## Assessment

The distribution of points for assignments for the semester and the tentative due dates are given below. The dates are subject to change.

Assessment Percentage Cumulative Date
Class Participation 10% 10% Ongoing
Problem Sets 20% 30% Various
Labs 10% 40% Various
Midterm 1 15% 55% T 27 Feb
Midterm 2 15% 70% Th 5 Apr
Report 5% 75% 3 May
Final exam 25% 100% 4-7 May (exam period)

Note: There will be no make-up exams for either of the two in-class exams.

• Class Participation: Class participation will be based on your participation in class, on Piazza, in study groups, office hours, etc.
• Problem Sets: There will be roughly 6 problem sets in the course. These are due at the beginning of class on the date indicated (unless otherwise noted on the problem set). Barring truly exceptional circumstances, late problem sets will not receive credit. As noted above, illness or missed classes do not count as exceptional circumstances. I will drop your lowest problem set score.
• Labs: There will be roughly 8 graded lab assignments in the course. Lab assignments will be due at the beginning of the next lab. Barring truly exceptional circumstances, late lab assignment will not receive credit. Note that in general, illness or missed classes do not count as exceptional circumstances. I will drop your lowest lab assignment score.
• Midterms: We will have two in-class midterm exams. They will cover particular topics prior to those midterms.
• Final: The final exam is cumulative and is self-scheduled during exam period.
• Report: You will statistically analyze a dataset that I provide you and give a report on the data with an economics-based policy recommendation.

## Group work on problem sets and labs

Working in groups on the problem sets and lab assignments is encouraged, but each student must prepare and submit her own answers. Copying your answers directly from another student or allowing a classmate to copy your answers are violations of the Honor Code. If you have any concerns about what constitutes independent work, please discuss them with me prior to the due date of the assignment.

# Course Policies

## Moodle & Website

In general, I will use my site, simondhalliday.com/eco220 for content for ECO220 and updating schedules. We also have a moodle site where I will put copies of files that we use in the course and where you will need to upload your assignments if we ask for electronic submission. I’ll explain why I use my website as a resource.

## Initial assessment

You need to complete the statistical knowledge assessment as soon as possible. It is not for grades, but it helps us to understand what the knowledge base for you is and where we can direct our efforts to ensure that we leave you with the best knowledge of statistics and econometrics at the end of the course.

If you have a disability and need accommodations in this course, please contact the O ce of Diasbility Services in College Hall or at ods@smith.edu as soon as possible to ensure we can implement accomodations in a timely manner.

As in any other course at Smith, you are required to adhere to the provisions of the Honor Code. I take academic honesty very seriously and will report any suspected violations of the Honor Code to the Honor Board. The two in-class exams in the course will be unproctored. The use of any unauthorized material or any discussion or copying of answers is strictly forbidden.

## Integrity in Empirical Research for your Team

When you set up a project, you will need to follow the TIER protocol. The draft protocol for R is available here: tier_protocol_r. Read it carefully and be sure that you know how to follow the protocol. Set up your project in a Google drive or Dropbox folder that follows the steps in the protocol. I shall also upload a folder on Moodle that provides a (somewhat updated) folder structure that we’ve agreed on since writing this initial draft. Also check the updated version online at the Project Tier Site.

You have a few deadlines that I have imposed on your Team Research Project.

• initial meeting with me
• team proposal presentation
• initial literature review submission
• confirm data import & initial replication exercise
• team final presentation
• final submission of project

I would suggest that you consider imposing deadlines within your team which you write up as a contract which all your team members agree to and sign. Provide me with a scan/photo of the agreement and submit parts of the project as the semester proceeds. You can amend the contract if everyone votes and agrees (send me a copy of the amendment). If you don’t vote to amend, then someone may fail to meet their contractual obligations. This happens all the time in teams, so please also be forgiving, but also let me know if this happens repeatedly and a group member does not do their agreed tasks.

## Team member evaluations

At the middle and end of the semester, you will evaluate each of your fellow team members in the following way. You will receive exactly these instructions on Moodle. For the mid-semester assessment, you will receive the feedback comments from others. For the final assessment, only I will read the comments.

“Evaluate the contributions of each person in your group except yourself, by distributing 100 points among them (that is, when you are done, the total points assigned to everyone should sum up to 100). You must provide comments for each person. These comments – but not who provided them – will be passed onto your teammates. Your score should reflect your judgment of such things as:

• Preparation (did they come to class prepared?),
• Contribution (did they contribute productively to group discussion and work?),
• Respect for others (did they encourage everyone to contribute and listen respectfully to different opinions?), and
• Flexibility (were they flexible when disagreements occurred?).

It is important that you differentiate between people who truly worked hard for the good of the group and those you perceived not to be working as hard on group tasks

(NOTE: If you give everyone pretty much the same score when it is not truly deserved, you will be hurting those who did the most and helping those who did the least)."

You can access the evaluation forms here:

## Stats Prep & Spinelli Center

In the first week of ECO254, you will need to complete a knowledge survey on Moodle. Please access the knowledge survey through Moodle.

Ms. Maria Delfin-Auza is the statistics consultant at the Spinelli Center. She has a BA in Economics and a BA in Math & Stats. She can coach you on the use of Excel, Stata and R.

## Revising and Learning Statistics

There are many resources online for learning or revising statistics.

• For introductory statistics, Open Intro Statistics is a free online textbook paired with R (and mosaic) that you can use to revise relevant statistical knowledge and applications.

• For the use of statistics in experiments, A First Course in Design and Analysis of Experiments is a textbook originally published in 2000 that has gone out of print, but the pdf of which has reverted to the author (Gary Oehlert) and which he has made available free of charge online under a creative commons license.

## Excel, Stata and R

During the course we will use R to do statistical analysis and produce graphics. R is rated among the top ten most useful programming languages and is growing in use. See for example, this blogpost: www.r-bloggers.com/r-6-in-ieee-2015-top-programming-languages-rising-3-places/

We need to do statistical analysis in the course, so you will learn about tidy data, the grammar of graphics and the basics of statistical analysis building on the theoretical knowledge you should have from ECO220 or MTH220. If you prefer to use Stata you are welcome to, but R is becoming more commonplace and there is more support for its use at Smith. Also, R and RStudio are free so you can access R using RStudio on your own computer. In contrast, Stata is costly and either the college or you yourself will have to pay for R.

We will use Microsoft Excel as a spreadsheet package for this course. You should also be able to use Google Docs as an alternative. I do not recommend MacOS Numbers: it is strictly inferior to both these alternatives. MS Excel is used in a variety of business, banking and accounting settings and I strongly advise you to improve your knowledge of the software. The main use of Excel will be to prepare data for use in R by exporting the data to a csv file. So you know, the following constitutes a non-exhaustive list of the functions I expect you ought to know how to use in Excel for the workplace, but which I shan’t go into myself in this course. corr, cov, sum, count, if, sumif, countif, concatenate, stddev, index, match, vlookup. (Maria will go through many of these in the Excel Workshop.)

For Help with Excel, Stata or R, I suggest you go to the following links:

• Excel, Stata and R Princeton’s Data and Statistical Services: They cover topics related to Stata and R and have very helpful annotated screenshots to help you undersand what’s going on. They have a helpful comparison document for Stata and R in case you happen to know the one package better than the other.
• R only The Five College Guide to R and R Studio: Covers the basics of what you want to be able to do in R-studio and R using the mosaic package. Prof. Horton also has a variety of very helpful videos on his webpage at Amherst for getting started with R (scroll about half-way down the page). He uses the lovely mosaic package to make R more accessible.
• Stata and R UCLA’s Statistics help pages: they have comprehensive help R, and for Stata. I use them regularly as reminders and tutorials.
• Stata only German Rodriguez’s online Stata tutorial at Princeton.
• Stata only Stata.com’s long list of resources for learning Stata.

Important Make sure you can save an Excel file as a comma separated value (.csv) file so that you can import it easily into either Stata (using the command insheet) or R (using the commands read.csv or read.table). To get help in Stata you can type in help followed by the command’s name e.g. help insheet. To get help in R you can type in ? followed by the command’s name, e.g. ?read.csv.

TIP If you want to import Stata data (a .dta file) into R, you should use the haven package.

## Style Guides

When doing statistical work, it is imperative that you adopt a good style when presenting your work. I recommend that you use a style guide.

For R Scripts:

Hadley Wickham has a brief and useful style guide. Google has a very comprehensive style guide for its employees who use R, Google’s R Style Guide

## Reproducibility and Integrity in Research

In ECO254, we shall do our best to follow the norms of the Teaching Integrity in Empirical Research (TIER) project in conjunction with the Open Science Framework (OSF).

## Quantitative Literacy/Quantitative Reasoning

“Economics is an empirically oriented discipline. The focus is on explaining and testing our understanding of economic phenomena. Hence, students need an appreciation for an ability to deal with empirical matters.” Siegfried et al 1991, p.216

“The foundation in empirical methods depends on (1) knowing something about the measurement of economic variables (methods of data collection, reliability, etc.); (2) being able to organize, work with, and manipulate data for purposes of comparison; (3) the capacity to test hypotheses with empirical data; and (4) knowing how to interpret the results of various statistical procedures. The quantitative methods course should be reoriented from its almost singular statistical focus to emphasize this wider range of quantitative methods employed by economics.”(ibid. p.216)

I will do my best to help you become more quantitatively literate and to help you to become better applied social scientists in your study of behavioral economics.

## ECO220 Master Tutor

The Economics Master Tutor for ECO220 is Richelle Ju. Her email is: rju50@smith.edu. She will be holding drop-in hours in Spinelli Center (days/time TBD: details will be posted at http://www.smith.edu/qlc/tutoring.html). You may also contact her directly (or the Spinelli Center) to make an individual appointment. You can find out more on the Spinelli Center Website: www.smith.edu/qlc/tutoring.html.

## Piazza, Questions & Email

All of which said, please feel free to email me. Typically, if an email is not about course content (which should almost always go on Piazza), then the email will be about something that is particularly relevant to you personally, e.g. you are traveling and will miss class, you need an extension for an assignment, you have a physical or mental health issue that needs to be resolved, etc. I shall always do my best to accommodate you. That said, I receive many, many emails. I try to ensure I get back to you within 24 hours (during the business week) or by Monday (if you emailed over the weekend). Occasionally, I may miss an email because of reading it on my phone and forgetting to mark it as unread to respond to it later. I apologize in advance if this happens.

## Some notes on our goals and our learning

• It is the first time I am teaching ECO220.
• I love statistics and its relationship to economics.
• As I will have many half-formed thoughts and draft ideas, forgive me if I get something wrong. I will do the same with you. Feel free to preface any such statement you make with “I have a half-formed thought,” or “I have a draft idea.” Encourage others who are willing to put their ideas out there and offer generous feedback.
• I am doing my best to provide a fantastic course.
• I want you to leave the course with some mastery of statistics and econometrics and a practical skill in the use of Stata.
• I am learning how best to provide such a course and how best to encourage student learning.
• So I am learning too. Please be forgiving because I am trying to learn enough to satisfy all of you, whereas you need to learn enough to satisfy one of me.

## Acknowledgments

In developing this syllabus, I have drawn on resources from variety of people: Vis Taraz, Mariyana Zapryanova, Jennifer Imazeki, Emily Marshall, and others who I probably don’t realize I pilfered from. Thanks to all of you.