Syllabus, Eco B2000, Colin Powell School at CCNY, Fall 2015

Tuesday 7:15-9:15pm, in NAC 6-114

Course Description

This MA course is designed to teach you to use the statistical tools that form an economist's basic toolbox in a hands-on environment working with a lot of real data.  Students will get a better understanding of statistics, of how numerical evidence is used and abused, and of how people can torture the numbers to make them appear to support their point of view.  In our modern world statistics are the first choice for how someone is going to lie to you.  If you know some of the secrets then you will be able to see through other people's lies (and perhaps create some of your own – if you choose to embrace the dark side!).

Textbook

There will be a diagnostic test at the beginning of term; students will use the online module on Statistics from Hawkes Learning http://www.hawkeslearning.com/.

This course uses the textbook by James H Stock and Mark W Watson, Introduction to Econometrics, Pearson.  Earlier editions are very close substitutes but you are responsible for ensuring the concordance to the most recent edition for homework assignments. You may wish to get the accompanying study guide, but it is not necessary. 

We will learn the R statistical language.  You might want to get A Beginner's Guide to R, by Zuur, Ieno and Meesters and/or Applied Econometrics with R by Kleiber and Zeileis.

Professor

Kevin R. Foster, Department of Economics and Business, The Colin Powell School for Civic and Global Leadership, The City College of New York, kfoster@ccny.cuny.edu, w: (212) 650-6201, m: (860) 593-7674, office hours Tuesday 4:30-6:30 in NAC 4-121 or by appointment.

Course Requirements/Prerequisites

This course assumes that you have prerequisites of a basic undergraduate course in statistics, a course in Calculus, and a familiarity with computers enough to quickly learn new programs.  I will use math freely and often.  Matrix algebra is not a prerequisite although I will occasionally use it to help more advanced students get a fuller understanding.  Stats requires a willingness to work through the algebra and plow through the applicable formulas.  However, that said, the point of doing it on a computer is so that the machine can do the donkey work while you worry about bigger questions – so what?  why?  what does it mean?  what else do I need to know?  what other hypotheses could present the same pattern?

 

Some course material will be presented in online videos.  All assignments will be submitted online.  All exams will be written on the computer and submitted online.

Educational Outcomes

Students will be able to apply mathematically rigorous analysis to topics such as hypothesis testing, common probability density functions, and regression analysis.  More details in the document, "Skills Learned in This Course," available from the course webpage.

Grading

Course grades are determined by four factors: your diagnostic test result, your scores on the exams, your demonstrated skill at using statistical analysis in a final project, and your scores on the homework assignments.  The exams have a 45% weight, the project has a 30% weight, homework gets 15%, and the diagnostic test is 10%.  Homework assignments and diagnostic test are graded as check, check-plus or –minus, or fail.  There is no BS factor of effort or any other unobservable will-o-wisps – the weightings sum to 100.  Your grade is determined entirely on observed performance.

 

You have the option of forgoing the homework assignments and having your grade determined only by test, exams and project.  This is unwise but you have the choice.  You must submit the online form to me early in the semester.

 

Grades will be posted on the course page, so that you can check your progress and determine what grade you can expect to receive. In this public grade posting, you will be identified only by the last 4 digits of your CUNYfirst ID number (if you wish to choose some other 4-digit identifier, email me).

Time Requirements

You should expect to spend 10-12 hours per week on this class.  My simple calculation is that a student who is going to school "full time" takes 4 or 5 classes and someone who works fulltime at a job works 40-50 hours per week.  So about 10 hours per week is a good estimate.  If you don't put in that much work then you can't expect to get a good grade.  (This is confirmed by research; on average a student studying one more hour per week can raise her term GPA by 0.36 – from a B to B+, for example.  Stinebrickner & Stinebrickner, 2008.  BE Journal of Econ Analysis & Policy, 8(1).)

Final Project

You will work with a small group of fellow students to write a project to analyze a question using one of the datasets that we'll be working with.  You will make a presentation about this project in class; the presentation counts as homework and then the written project has a separate grade.  This will require you to use statistical analysis software with a large dataset.  More details will be given later in the course.

Course Material

Homework and basic course documents will be on the class page, publicly accessible from my web page (http://kfoster.ccny.cuny.edu/).  Some of the material will be available on the Blackboard course page (login required).  I will periodically send emails to the class via Blackboard so you must keep your CCNY email updated.

Computer Use

This course will use R, data analysis software that is freely available from http://www.r-project.org/ along with the R-Studio interface.  I recommend you download and install R and R-Studio if you have a personal computer.  The programs are available at various campus computer labs as well.  There are many resources available online to learn R.  You are not required to have previous experience with programming although that would be useful.  There will be numerous web videos explaining the basics of how to use R in statistical analysis.

Additional Reading

If you end up engulfed in a love affair with stats, you might be interested in these books too:

Leonard Mlodinow, The Drunkard's Walk: How Randomness Rules Our Lives

John W. Tukey, Exploratory Data Analysis (in library)

Edward R. Tufte The Visual Display of Quantitative Information, Visual Explanations: Images and Quantities, Evidence and Narrative (in library);

Howard Wainer, Graphic Discovery: A Trout in the Milk and Other Visual Adventures

David Salsburg, Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century

Stephen Stigler, Statistics on the Table (in library) and The History of Statistics: The Measurement of Uncertainty before 1900 (in library).

Dierdre McCloskey , Economical Writing and The Rhetoric of Economics (in library).

Peter Kennedy, A Guide to Econometrics.

Joshua Angrist & Jörn-Steffen Pischke, Mostly Harmless Econometrics: An Empiricist's Companion.

Andrew Gelman and Jennifer Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models.

Cosma Rohilla Shalizi, Advanced Data Analysis from an Elementary Point of View.

John Hopcroft and Ravindran Kannan, Foundations of Data Science.

DJ Patil and Hilary Mason, Data Driven: Creating a Data Culture.

 

 

 

Weekly Topics

Eco B2000, Fall 2015

Kevin R Foster, CCNY

Date	Chapter(s)	Topic
Sept 1	1, online notes	Introduction to Econometrics, R
Sept 8	2, 3; Hawkes	Basic Statistics & Random Variables
Tues Sept 22		Diagnostic Test must be completed before midnight (local time)
Sept 15		no class
Sept 22		no class
Sept 25 (Fri)	online notes	Estimating Parameters
Sept 29	online notes	Hypothesis Testing
Oct 6	4, 5	Linear Regression
Oct 13	6, 7	More Regression
Oct 20	1-7 and online	Exam 1
Oct 27	online notes	Further Topics
Nov 3	8, 9	Nonlinear Regression, Panel Data
Nov 10	10, 11, 12	Binary Dependent, Instruments, Quantile, Propensity Score
Nov 17	online notes	Further Topics: Trees, Random Forest, LASSO, Spike & Slab, Factor Analysis, (Time Series)
Nov 24	comprehensive	Exam 2
Dec 1		Class Presentations of Research Projects – attendance is graded
Dec 8		Class Presentations of Research Projects – attendance is graded
Dec 23		Final Project Due before midnight

 

Chapters refer to Introduction to Econometrics, Stock and Watson, 3^rd edition.

 

There will be lecture notes available online – these are most important.  Exams will cover material in both textbook and lecture.

 

Deviations from the schedule will be announced in class.

 

The exam dates are given above. You must take the exams at the scheduled times. No excuses.

 

 

 

Academic Integrity

The CCNY Faculty Senate has recommended that every course syllabus include this notice:

CUNY Policy on Academic Integrity

As stated in the CUNY Policy on Academic Integrity: 'Plagiarism is the act of presenting another person's ideas, research or writings as your own. The following are some examples of plagiarism:

• 'Copying another person's actual words without the use of quotation marks and footnotes attributing the words to their source;
• 'Presenting another person's ideas or theories in your own words without acknowledging the source;
• 'Using information that is not common knowledge without acknowledging the source;
• 'Failing to acknowledge collaborators on homework and laboratory assignments.
• 'Internet plagiarism includes submitting downloaded term papers or parts of term papers, paraphrasing or copying information from the internet without citing the source, and "cutting & pasting" from various sources without proper attribution.'
• A student who plagiarizes may incur academic and disciplinary penalties, including failing grades, suspensions, and expulsion.
• A complete copy of the CUNY Policy on Academic Integrity may be downloaded from the College's home page.   

Accommodations for Students with Disabilities

The Office of Student Disability Services (SDS) is dedicated to providing students with disabilities equal access to the College curriculum. The Office ensures that, upon request, qualified students with disabilities are provided reasonable and effective accommodations, as mandated by law, as well as appropriate support services.

 

Students who contact SDS and indicate that they have a disability or believe that they might qualify for services will be asked to make an appointment for an intake interview with SDS staff. To qualify for services, students must register with SDS by providing appropriate documentation from a qualified professional describing the nature of their disability and functional limitations. Although academic adjustments are mandated by law, the College is not required to alter demonstrably essential academic requirements of a course of study nor is the College mandated to lower or effect substantial modifications of reasonable academic standards.

 

Early planning is essential for many of the resources, adjustments and accommodations; students are asked to contact SDS at the earliest possible date (NA 1/218; 212-650-5913 or 212-650-6910 for TTY/TTD).