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Organizing Information
(posted: 01 Dec 2017)
I want you to learn econometrics and the best way to learn econometrics is to do it.
But more broadly, I hope that conducting an econometric analysis will teach you how
to organize information.
In the specific case here, each column in your spreadsheet represents a variable
and each row represents an observation, so your task is to properly align the
information that spreadsheet.
If you carefully construct that initial spreadsheet from reliable sources of data
(and if you choose a good set of variables to test your null hypothesis),
you should observe some clear trends in your data. Your task then is to
explain those trends, test your null hypothesis and report your findings.
Gretl, of course, will help you run regressions and calculate statistics for your analysis.
But Gretl is a tool. It is not the tool that is important. It is the quality of your input
that is important.
That initial spreadsheet is what's important.
How you organize information is what's important.
In a more general case, your information might not be the numeric data that we work
with in econometrics. It might be names, addresses or whole documents and files.
Your data might not even fit into a spreadsheet at all.
But some principles, like functions and variables, will remain the same.
And, once again, what will be important is how you organize information.
For example, consider a different problem. Suppose you want to know what words
are most commonly used to describe a product that you are selling or a stock in
your portfolio.
Here, you must conduct a statistical analysis of words. To conduct such a statistical
analysis of nonnumeric data, what will be important is how you organize information.
Since the domain of our functions will be a word (not a number), we must define our words.
Just as real numbers may be integers, rational numbers, irrational numbers, etc.,
our words may be nouns, verbs, adverbs, adjectives, etc.
That's why I am
annotating
the Sicilian language.
Using that index,
I can define most of the language in a very short amount of time.
And with all of those definitions, we can conduct a statistical analysis.
(e.g. of Sicilian Wikipedia).
Which words are the most commonly used words? Which words are the most common objects of a
particular verb or of a particular preposition? Which adjectives are most frequently used
to describe a particular noun? Which adverbs ... ?
How do the words used to describe a stock affect its price?
How much do they affect its price?
We can find the answers to these questions, if we organize our information.
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Course Project
(posted: 20 Nov 2017)
I know that some students are making good progress on the course project.
And I know that the course project is giving some students a lot of stress.
This is a difficult assignment. Stress is normal.
If you are feeling stressed, please tell me how I can help you.
If you are struggling to assemble a dataset, please consider using the
USA state panel with employment
rates (from BLS) and minimum wage rates (from Vaghul and Zipperer).
If you use the USA state panel for your analysis, first try to reproduce
the regression results that I provide in the
documentation.
Then look for other ways to test the null hypothesis that the
minimum wage rate does not affect the employment rate.
Most importantly, please tell me how I can help you.
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December Sessions
(posted: 20 Nov 2017)
When we return from Thanksgiving break, we will wrap up our discussion of
probability models.
To prepare for that discussion,
please read StockWatson chap. 9 and Kennedy chap. 16.
Finally, we will discuss timeseries.
To prepare for that discussion, please read
StockWatson chaps. 12, 13 and 14 and Kennedy chaps. 10 and 19.
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Thanksgiving Break
(posted: 20 Nov 2017)
Queens College will be closed from
Thu 23 to Sun 26 Nov. No classes will meet.
Enjoy the break! Have a Happy Thanksgiving!
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Employment Rates
(posted: 03 Nov 2017)
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WLS and the Logit Model
(posted: 31 Oct 2017)
As a followup to our discussions of weighted least squares, I have prepared
some notes on: heteroskedascity
in the logit model.
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GaussMarkov Assumptions
(posted: 30 Oct 2017)
W.H. Greene's Econometric Analysis has an excellent summary of the
GaussMarkov assumptions:
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Violations of GaussMarkov
(posted: 24 Oct 2017)
Now that we have finished our discussion of the classic linear regression model,
which assumes that GaussMarkov assumptions are satisfied, we will begin exploring
violations of the GaussMarkov assumptions.
For theoretical background, please read Kennedy chaps. 5, 6 and 7.
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Midterm and Project Proposal
(posted: 24 Oct 2017)
Having passed the midpoint of the semester, I now want you to compile your
notes on the topics that we have discussed so far. For that purpose, I would
like you all to submit handwritten notes on the midterm exam questions
listed at the end of the syllabus.
The best way to learn econometrics is to conduct an econometric analysis,
so I am looking forward to reading your project proposals. And I hope that
you are looking forward to conducting the analysis because this is fun.
For the project proposal, I would like you to submit a written description
of the null hypothesis that you wish to test and the dataset that you plan
to test it with. More details are listed at the end of the
syllabus.
If possible, I would like you to submit the midterm and the proposal on
Sun. 05 Nov. and Tues. 07 Nov. I will happily grant extensions to that
deadline if necessary, but let's all try to submit them on those dates.
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Hypothesis Testing
(posted: 02 Oct 2017)
We will finish our discussion of the
problem set
during Sunday's session (01 Oct) and Tuesday's session (03 Oct).
Then during the next sessions (on Sun 08 Oct and Tue 10 Oct),
we will discuss
hypothesis testing.
For theoretical background on hypothesis testing,
please read Kennedy chap. 4.
Please also read either StockWatson chaps. 5 and 7
or equivalent chapters in a better textbook.
Two alternatives that you might consider are the
Hill, Griffiths and Lim
textbook and the
Studenmund textbook.
And for an empirical example of hypothesis testing, please read my
Analysis of the "Biagi Law".
Because I will frequently use those
datasets
in examples of several different econometric topics,
you should also read my note about
teaching
with the "Biagi Law" data.
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Next Class Sessions
(posted: 21 Sept 2017)
During our next class sessions  on Sun 24 Sept and Tue 26 Sept  we will
continue our discussion of the problem set
with problems #2 and #3, which are designed to help you understand
maximum likelihood and
hypothesis testing.
For background on those topics, please read Kennedy chaps. 14.
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On Textbooks
(posted: 21 Sept 2017)
It is important for students to purchase copies of both textbooks
and read them both. In practice, it is easy to convince students to
read the Kennedy textbook, but it is difficult to convince students to read
the StockWatson textbook.
So maybe we should replace the StockWatson textbook?
If you're interested in exploring some alternatives,
check out the one by
Hill, Griffiths and Lim and its Gretl companion by
Lee Adkins (PDF).
Or check out the
Studenmund textbook.
And as you search for alternatives, compare reviews.
Readers give better reviews to the HillGriffithsLim textbook
and the Studenmund textbook than they do to the
StockWatson textbook.
Finally, please share your thoughts on these textbooks with me,
so that I can design a better course. Thank you!
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Problem Set and Reading
(updated: 06 Sept 2017)
On Tuesday (05 Sept), I introduced the
problem set.
I will introduce it to the Sunday group when we next meet (10 Sept).
The problem set will help you understand the material in chaps. 15
in the StockWatson textbook. So please read those chapters and
please work on the problem set.
For econometric examples, you know that I like to use the question of
how labor market regulations affect employment outcomes, so please
also read Schmitt's discussion
of those effects and please read my
Analysis of the "Biagi Law".
I have also prepared some
documentation of the datasets
used in class, so please look at that one too.
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Employment Rates and the Minimum Wage
(updated: 05 Sept 2017)
update: I have posted an
unfinished draft
of a paper that documents the labor market data that I collected,
summarizes the statistics that I calculated from that data and
reports a few of my findings.
...
As a followup to our discussion of the effect that the minimum wage has on employment rates,
I combined Vaghul and Zipperer's (WCEG, 2016)
minimum wage data with the
BLS employment status by state data.
The combined data is available as a
CSV file
and as
Gretl data.
I have also made available the
R script
that I wrote to analyze the combined data.
While examining the data, I noticed that 9 of the 11 states that joined the Confederacy had the
lowest minimum wage rates in the country (and the other two weren't much higher), so I grouped the
states by Civil War status and obtained the following weighted averages for the period 20012016:
 
 

employ. rate 
state minimum wage 
average annual pay 
 
 
Free States  61.0%  $7.06  $48,306 
Border States  60.7%  $6.47  $42,012 
Confederacy  59.6%  $6.43  $41,738 
New States  62.6%  $6.91  $41,427 
 
 
To be fair, some Free States do have a low minimum wage rate,
but all of the Confederate States have low minimum wage rates.
The Free States also tend to have higher employment rates than
the Confederate States.
And overall, states that have higher minimum wage rates
tend to have higher employment rates.
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First Assignments
(posted: 29 August 2017)
As a followup to Sunday's session and a preview of Tuesday's session,
I would like to continue our discussion of
statistics and probability and then
introduce ordinary least squares.
To prepare for those sessions, please read chaps. 15 in the
StockWatson textbook. And please take first look at the
problem set.
For econometric examples, I like to use labor market data and
I'm particularly interested in the question of how labor market regulations
affect employment outcomes. For theoretical background, please
review my minimum wage and monopsonist problem
and then read Schmitt's discussion of the effects of
the minimum wage on employment and my
Analysis of the "Biagi Law".
Finally, please be aware that the minimum wage in St. Louis
fell from $10 to $7.70 yesterday.
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Calendar Notes
(posted: 29 August 2017)
Please note that Sunday's group will not meet on 03 Sept
(in celebration of Labor Day) and Tuesday's group will not
meet on 19 Sept (in celebration of the Jewish new year).
For a complete information, please see the
Queens College Calendar.
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Welcome to Econometrics
(posted: 26 August 2017)
Welcome to the course website. This site helps me organize the course.
I hope you find it helpful.
I have posted a copy of the
syllabus.
Please review it and please look at the notes and course materials
that I have posted below. I have also prepared a
problem set for you.
We will discuss the solutions to these problems in class.
Please acquire a copy of the
Stock and Watson textbook and the
Kennedy textbook as soon as possible.
To begin the course, I would like to start with a review of
statistics and probability, so please read
chaps. 1, 2 and 3 in the Stock and Watson textbook
before our next class session. To refresh your memory of statistics,
you may want to look at my
statistics course.
datasets
You must do econometrics to learn econometrics. A textbook is
helpful, but not sufficient. To learn econometrics, you must
actively explore a dataset.
The best dataset to give you is the dataset that I am most
passionate about. I want to know how employment protections affect
the employment opportunities available to workers.
This is a personal issue for me, but it's also an issue that affects
you, your family and your friends, so together let's explore the data
and find out how labor law shapes the career opportunities available
to you.
So as a starting point for discussion and as an introduction to
econometrics, please read my
"Analysis of the 'Biagi Law.'"
It tests an important null hypothesis and the dataset that the paper
explores is one of the datasets that we will explore together this
semester.
Then to place the discussion in an American context, please read
John Schmitt's (2013) paper on why the
minimum wage has no discernible effect on employment.
Having identified an avenue of inquiry, we must place it in a
theoretical context to, so please read the
wikipedia article on the minimum wage
and please study my "minimum wage
and the monopsonist" problem.
With theory and data in hand, we will then test the null hypothesis
that the minimum wage does not have a significant effect on
employment rates.
software
To conduct a statistical analysis (like the one just described),
you will need to install a few (open source)
statistical and mathematical software programs on your computer.
Gretl
is a great statistical software package for learning econometrics.
It has an simple interface and ships with sample datasets.
It's a great learning tool and I highly recommend it.
As you get better at data analysis, you will want a
more flexible tool. I use the
R language,
which (as the name suggests) is a programming language.
For mathematics, I use
wxMaxima,
which provides a graphical interface to
Maxima,
a computer algebra system that specializes in symbolic operations
(as opposed to numerical computing).
Over the course of the semester, I will provide datasets for you
to work with in Gretl. I will avoid asking you to write your own
computer code, but to push you in the right direction,
I will provide R scripts and wxMaxima notebooks for you to review
(and tinker with).
take your time
Finally, please remember that we will cover these topics
over the course of the semester. So please begin reading,
but please do not rush through the reading. Take the time
to understand what you are reading. And enjoy it because
econometrics is a lot of fun.
I'm looking forward to working with you this semester.
Sincerely,
 Eryk Wdowiak
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