CHAPTER 17
Program Evaluations
1. Introduction: Program Evaluations
In this section, we study the methods of program evaluations. Suppose that a selected
set of individuals receive training or education initiated by the government with a view
to enhancing
CHAPTER 15
Binary Choice Models
1. Binary Dependent Variables
We have studied mainly the situation where the dependent variable Y is a continuous
variable. However, in many empirical researches, the dependent variables are discrete, or
more often, binary.
206
14. PANEL LINEAR REGRESSION MODELS
3.2. Fixed Effects with Lagged Dependent Variable
An alternative way of viewing a fixed effects panel model is to look at it from the
perspective of temporal dependence of outcomes (or dependent variables). One of th
198
14. PANEL LINEAR REGRESSION MODELS
2.4. An Alternative Approach : First Differencing
We saw that under Assumptions FE1-FE2, we can use within-group transforms to
identify the coefficient 1 and estimate it consistently. The within-group transform is ma
CHAPTER 14
Panel Linear Regression Models
1. Panel Data
Panel data consist of a set of individual observations that are observed over a period of
time. For example, the observations take the form of (Yit , Xit ), i = 1, , n and t = 1, , T,
where i denotes
7. CHECKING THE VALIDITY OF INSTRUMENTAL VARIABLES
181
have better quality than the sample analogue estimators that we mentioned before. Proving
this, however, belongs to the graduate course material.
7. Checking the Validity of Instrumental Variables
In
2. INSTRUMENTAL VARIABLES
165
We focus on the case where Cov(ui , wi ) = 0 and Cov(wi , Xi ) = 0 more clearly. In this
case, the asymptotic bias of the regression with measurement error is
i)
Cov(vi , X
Var(wi )
= 1
.
i)
Var(wi ) + Var(Xi )
Var(X
Therefor
CHAPTER 13
Regression Models under Endogeneity
We have studied univariate and and multiple regression models under the assumption
that the unobserved component ui in the regression model satisfies that
E [ui |X1i , , XKi ] = 0
where X1i , , XKi are regres
CHAPTER 9
The Properties of Least Squares Estimators
1. Unbiasedness
In this section, we investigate the properties of the least squares estimators. We recall
the regression model:
Yi = 0 + 1 Xi + ui
where
Condition 1:
Condition 2:
E[ui |Xi ] = 0 and
V ar
220
15. BINARY CHOICE MODELS
Note that the specification of the choice probability and the partial effects crucially
hinges on the assumption that ui is standard normal. An alternative specification is possible, for example, by assuming that ui actually f
CHAPTER 17
Program Evaluations
1. Introduction: Program Evaluations
In this section, we study the methods of program evaluations. Suppose that a selected
set of individuals receive training or education initiated by the government with a view
to enhancing
Part 1
Probability Basics
CHAPTER 1
Probability
1. What is Probability?
1.1. Probability. Many decision problems arise in uncertain situations. To evaluate
decisions and choose "reasonable" ones, one needs to be equipped with a coherent way of
expressing
Econ 527
Assignment 5
The due date for this assignment is Thursday October 13.
1. Davidson and MacKinnon, Chapter 2: Exercises 2.15, 2.16, 2.17, 2.18, 2.20.
2. Consider a partitioned linear regression model Y = X1 1 + X2 2 + U , and assume
that the assump
Econ 527
Assignment 4
The due date for this assignment is Thursday October 6.
1. Davidson and MacKinnon, Chapter 2: Exercises 2.11, 2.12, 2.13, 2.14, 2.23, 2.24.
2. Davidson and MacKinnon, Chapter 3: Exercises 3.11, 3.15, 3.16.
3. Let X be an n k matrix o
Econ 527
Assignment 3
The due date for this assignment is Thursday September 29.
1. Davidson and MacKinnon, Chapter 2, Exercises 2.2, 2.3, 2.52.8.
2. Davidson and MacKinnon, Chapter 3, Exercises 3.63.10. Hint: In Exercise 3.8, A
is symmetric and positive
Economics 526 - Mathematics for Economists
Term 1, 2016
Paul Schrimpf
This is a course of mathematics for students in our Economics MA program. It covers powerful mathematical tools that often appear in the modern economic literature. It is essential for
Econ 425 - Problem Set 9
Due March 31 (Thursday) in Class
Instructions. Do your best to make your arguments rigorous. You may discuss this problem
set with your classmates and consult any books or notes, but write out the answers on your own
using your ow
Econ 425 - Problem Set 7
Due March 17 (Thursday) in Class
Instructions. These questions review linear endogenous regression models. Do your best to make
your arguments rigorous. You may discuss this problem set with your classmates and consult
any books o
CHAPTER 10
Multiple Linear Regression Models
In this chapter, we study inference from the muliple regression model. A full exposition
of this model requires the language of linear algebra which we try to avoid using in this
course. Hence, the analysis in
4. THE APPROACH OF PROPENSITY SCORES
247
4. The Approach of Propensity Scores
The switching regression model approach uses assumptions to identify the average treatment effect as a regression coefficient to the treatment status variable Di . Among the
ass
Part 3
Linear Models
In this part, we study linear regression models and understand the meaning and the role
of the basic assumptions. We study how we define the parameter of interest and interpret
the parameter, and how we come up with a systematic proce
CHAPTER 3
Families of Discrete Distributions
1. Bernoulli Distributions
Once we know the distribution of a random vector, we can immediately compute the
probabilities that involve the random vector. However, in many situations, knowing fully
the distribut
Econ 425: Midterm
February 13, 2014, Tuesday
Time Limit : 60 minutes. Total 100 Points
Instructions :
(A) You will be given rst 10 minutes as a reading time that is not included in the one-hour
exam time. During this reading time, you can read the exam an
Econ 425 - Problem Set 6
Due March 13 (Thursday) in Class
Instructions. Do your best to make your arguments rigorous. You may discuss this problem
set with your classmates and consult any books or notes, but write out the answers on your own
using your ow