Regression with One Variable notes
I.
Curve-fitting
A.
B.
With three or more points, it will normally be impossible to fit it
exactly.
C.
It is possible to draw numerous lines through a bunch of points,
but which is the "best" line describing the behavior
Intro to Econometrics
Lecture 6: March 22, 2016
Logistics Midterm
Midterm is March 29, 7:20-9:20pm.
The midterm will cover everything we have
done up until (and including) today.
When you study for the test, focus on what
we have been doing in classes,
Intro to Econometrics
Lecture 4: Mar 1, 2016
Overview of tonight
1. Quiz
2. Bivariate regression: hypothesis tests and
confidence intervals
3. Dummy variables in bivariate regression
4. Homoskedasticity and heteroskedasticity
Quiz
Bivariate regression: hy
Intro to Econometrics
Lecture 10: Apr 26, 2016
Overview of tonight
Quiz
Instrumental variables, IV regression
Quiz
Instrumental variables,
IV-regression
Introduction
We are interested in the following regression:
= 0 + 1 1 + 2 2 +
However, we are co
Intro to Econometrics
Lecture 2: Feb 9, 2016
Logistics
Can you access the course material on
Blackboard?
Let me know if you experience problems.
Do you have access to STATA?
Let me know if you experience problems.
Overview of tonight
1. Quiz
2. Review
Intro to Econometrics
Lecture 1: Feb 2, 2016
Overview of tonight
1. Logistics
2. Why econometrics?
3. Review of statistics, part 1
Logistics
Stata
An important part of this course is to make you familiar with the
software package Stata.
This is one of t
EconometricsSummer 2016
Problem Set 3
1.) Open the data for the cigarette tax problem used as an example in class in Excel
(state_cig_data.csv).
a.) Use Excel to construct two new variables: The natural log of per capita consumption
ln(Q) and the natural
EconometricsSummer 2016
Problem Set 2
1) Load the data from elmhurst.csv into DataSplash and run a regression with
gift aid as the dependent variable and family income as the independent
variable. Print the results. Is the trend negative or positive? How
Intro to Econometrics
Lecture 5: March 15, 2016
Overview of tonight
1. Quiz
2. Multivariate (multiple) regression
3. Omitted variable bias
Quiz
Multivariate (multiple) regression
Introduction
Remember that our goal is to capture the
causal effect of one
Intro to Econometrics
Lecture 3: Feb 23, 2016
Overview of tonight
1. Quiz
2. Bivariate regression
3. Assessing the work of others an example
4. A second look at Stata: running regressions
Quiz
Bivariate regression
The origin of regression
In 1886 Francis
Omitted Variable Bias notes
I.
Omitted Variable Bias
A.
B.
Your results are biased, however, if your coefficients are wrong for
reasons OTHER than random error.
C.
The most common bias is called "Omitted Variable Bias."
D.
This means that you "omitted" (l
Multiple Regression notes
I.
Intuition Behind Multiple Regression
A.
The simple regression model estimates a dependent variable as a
function of ONE independent variable.
B.
This is unsatisfactory because frequently more than one factor
matters!
C.
A mult
Random Walk Hypothesis notes
I.
Astrology and Financial Markets
A.
There are innumerable highly paid experts on financial markets
who claim to have special insight into the future.
B.
Technical analysts try to extrapolate trends, etc.
C.
Fundamental analy
Phillips Curve notes
I.
Economic Theory of the Simple Phillips Curve
A.
Review of Aggregate Demand and Aggregate Supply
B.
Classical AS curve is vertical; Keynesian AS curve is upward
sloping (or horizontal!)
C.
The same is true for Classical vs. Keynesia
Monetarism notes
I.
Monetarism vs. Keynesianism
A.
Monetarists and Keynesians both accepted the AS-AD framework,
and agreed that AS was not vertical (in the short to medium run).
B.
The disagreement: What shifts AD?
C.
In other words: What determines Nomi
Economic Freedom notes
I.
Measuring Economic Freedom
A.
Several major historical events have suggested to many that
economic freedom causes prosperity.
A.1.
U.S. in 19th century
A.2.
"Asian Tigers" after WWII
A.3.
West vs. East Germany; North vs. South Ko
Dummy Variable notes
I.
Dummy Variables
A.
B.
II.
We have already briefly discussed dummy variables. A dummy
variable is simply a variable that can either be 0 or 1.
A dummy variable is used to put "discrete" variables into a
regression model. Most variab
Correlation vs Causation notes
I.
Derivation of the Standard Errors for Slope and Intercept
A.
It turns out to be important to estimate the variance of the error
terms. This can be done using a simple
formula:
, where k is the number of
independent variab
Derivation of the Slope and Intercept Terms notes
I.
Derivation of the Slope and Intercept Terms
A.
Standard minimization technique: take the partial derivatives wrt
the variables you are minimizing over:
the equation equal to 0.
B.
C.
Simplifying:
D.
Mul
Endogenous vs Exogenous Variables notes
I.
Endogenous vs. Exogenous
A.
Statistics by itself just gives correlation, but usually causation is
what is interesting.
A.1.
B.
Exception: Forecasting.
There are many techniques you can try to see if a relationshi