NONPARAMETRIC ESTIMATION OF A CONDITIONAL MEAN FUNCTION
Let Y and X be random variables. These notes are concerned with estimating the
conditional mean function g ( x) = E (Y | X = x) without making a
LN6: Heterogeneity
6
A. Torgovitsky
Lecture Notes #6: Heterogeneity
6.1
Linear Regression
In the course of studying the linear model in this class and your previous classes, we have sometimes
consider
LN1: Linear Regression
1
A. Torgovitsky
Lecture Notes #1: Linear Regression
1.1
Causal Effects
In your previous econometrics courses, you have studied several variants of the linear regression
model
Y
LN3: Nonparametric Regression
3
A. Torgovitsky
Lecture Notes #3: Nonparametric Regression
3.1
Introduction
One aspect of the multiple linear regression model that can be controversial for causal analy
LN4: Panel Data
4
4.1
A. Torgovitsky
Lecture Notes #4: Panel Data
Introduction
So far we have been working with cross-sectional data, where we observe every unit once. This data
comes in the familiar
LN8: Regression Discontinuity Designs
8
A. Torgovitsky
Regression Discontinuity Designs
Another common empirical strategy for binary treatments is a regression discontinuity design
(RDD). An RDD can b
LN2: Monte Carlos
2
A. Torgovitsky
Lecture Notes #2: Monte Carlos
2.1
Introduction
A Monte Carlo study is a computer simulation used to investigate the properties of an estimator.
The primary purpose
4. Suppose that we observe an i.i.d. sample (Yi , Xi ), i = 1, . . . , N where Yi is yearly income
at age 30 and Xi is a discrete variable that indicates college major choice from among
K college majo
4. Suppose that we observe an i.i.d. sample (Yi , Xi ), i = 1, . . . , N where Yi is yearly income
at age 30 and Xi is a discrete variable that indicates college major choice from among
K college majo
LN7: Difference in Differences
7
A. Torgovitsky
Difference in Differences
The focus of this class has been on alternative models that can be used to identify and estimate
the causal effect of an expla
Middle East Technical University
Department of Economics
Fall 2014
Econ 502-Macroeconomic Theory I
Date: 25.12.2014
Date due: 10.01.2015, nal exam time
PROBLEM SET #5
(Questions with (*) sign are Home
Applied Econometrics 281, Handout 2
1
Joint distributions
1. Consider the joint distribution of income X and education Y .
Y =1 Y =2 Y =3
X=5
0.22
0.15
0.08
X = 10
0.11
0.14
0.10
X = 20
0.05
0.07
0.08
NOTES ON KERNEL NONPARAMETRIC REGRESSION
The problem is to estimate the function g in the model
Y = g ( X ) + U ; E (U | X ) = 0 .
In other words, we want to estimate the conditional mean function g (
THE PE TEST
Consider a linear and log-linear model. ssume for simplicity that there is only one righthand side variable. Additional right-hand side variables can be included in the usual way.
The line
THE RESET TEST
This is a test of the hypothesis that a regression model has the right functional form. Let
the model be
(1)
Y = 0 + 1 X 1 + . + K X K + U ; E (U | X 1 ,., X K ) = 0 .
In this model, Y
NOTES ON THE MULTIVARIATE NORMAL AND CHI-SQUARE DISTRIBUTION AND ON
THE POWER OF HYPOTHESIS TESTS
I.
The Multivariate Normal and Chi-Square Distributions
A. A random vector X with dim( X ) = d has the
Econometrics 381: Section 4
January 28, 2011
1. Practice problem 1:
Let Y = 0 + 1 X + U and E(Ui |X) = c, E(Ui2 |X) = 2 (Xi X)2 + c2 , where c is some
constant dierent from 0. Suppose you have a rando
LN5: Instrumental Variables
5
5.1
A. Torgovitsky
Lecture Notes #5: Instrumental Variables
Introduction
Our focus has been on the linear regression model
Yi = + Xi + Ui ,
(1)
where is the causal effect