Generalizations of Least Squares
We begin this lecture with a discussion of the eciency of the OLS estimator which we use to motivate
a discussion of weighted least squares. We then discuss some furth
Hazard Models
This nal lecture provides a brief introduction to hazard models, which are statistical models for explaining spell lengths (e.g. unemployment duration or the length of an individuals lif
Econ 244, Lecture I: Regression, Selection on
Observables, and Quantiles
Chris Walters
University of California, Berkeley
September 18, 2015
Introduction
Regression
Selection on Observables
Distributi
Econ 244, Lecture II: Instrumental Variables
Chris Walters
University of California, Berkeley
September 18, 2015
Introduction
Constant Effects
Heterogeneous Effects
Introduction
This lecture covers in
Panel Data II
In this lecture we will review the material in Chamberlain (1984)s famous handbook chapter on panel data
and briey cover some dynamic models. We start with correlated random eects estima
Econ 244
Applied Econometrics
Fall 2014
Patrick Kline
[email protected]
Overview: This course surveys modern methods of microeconometric research with an emphasis
on linking formal econometric theor
Econ 244, Lecture III: Sample Selection Models
Chris Walters
University of California, Berkeley
September 25, 2015
Introduction
Heckit
Equivalence
Extrapolation
LIV
Multiple Treatments
Introduction
Th
Linear Regression Model
y = X

+
i ~ iid(0, 2) ! E() = 0, Var() = 2INxN
E(Xiki) = 0 for all k
X has full column rank K
^
^
ols = ( X X )1 X y , E ols =
^ ^
Var ols = 2 ( X X ) 1 ,
^
^2
=
^ ^
n
University of California, Berkeley
Department of Economics
Ken Chay
Fall Semester, 2005
ECON 244
APPLIED EXERCISE #3
Due 4 pm on November 21, In Ken Chays mailbox
This exercise examines the following
Lecture: Selection on Observables
(Hand in P.S. #1)
Evaluation/Selection Problem:
Ex. Linear additive model
yi = + Ti + X i + i
Focus on binary (01) treatment, homogeneous treatment effects
1, if tr
Regression Discontinuity II
Pat Kline
Topics
Special topics in RDD
Quantile RD
Multiple Discontinuities
RKD
Fuzzy / Sharp
Estimation
Inference
Beyond Means
Standard RDD can be used to identify
Panel Data I
In this lecture we will review linear panel data models based upon variance component representations of
the DGP. We will then discuss a number of practical issues that frequently arise i
Econ 244, Lecture IV: Regression Discontinuity
Chris Walters
University of California, Berkeley
October 2, 2015
Introduction
Sharp/Fuzzy RD
Diagnostics
Estimation/Inference
Examples
Introduction
The r
Panel Data I
In this lecture we review linear panel data models based upon variance component representations of the
DGP. We then discuss a number of practical issues that frequently arise in estimati
Panel Data II
In this lecture we will review the material in Chamberlain (1984)s famous handbook chapter on panel data
and briefly cover some dynamic models. We start with correlated random effects es
Structure, Design, and Causality
This lecture introduces basic concepts in econometric modeling and their relationship to modern notions
of causality. In the process, we lay the foundation for many of
Estimation Principles
In this lecture we will review general estimation principles. It is worth reading this lecture along with
Newey and McFadden (1994)s famous Handbook of Econometrics chapter (from
Homework #1
Due in class 9/19/14
Please work in groups of 24
Answers must be typed
1. Identication I (OLS). Consider the linear regression model:
Yi = Xi + ui
where Yi and ui are scalar random variab
Homework #2
Due in class 10/10/14
Answers Must be Typed
Work in Groups of 24
1) Consider the following Table from Fehr and Goette (2007) reporting descriptive statistics from an
individuallevel rand
Homework #3
Due 11/21/14
1) (Reweighting) Go to David Autors webpage and download the cleaned
1979 and 1997 MORG les from:
http:/econwww.mit.edu/faculty/dautor/data/autkatkear08
a) Read through the c
Homework #4
Due in class 12/15/14
1) (CRE vs Fixed Eects)
Consider the model
Yit = i + Xit + it
where i is a xed person eect, Xit is a scalar regressor, and it is a strictly exogenous time varying err
Structure, Design, and Causality
This lecture introduces basic concepts in econometric modeling and their relationship to modern notions
of causality. In the process, we lay the foundation for many of
Regression as Reduction
In order to arrive at a distinct formulation of statistical problems, it is necessary to dene
the task which the statistician sets himself: briey, and in its most concrete form
Estimation Principles
In this lecture we will review general estimation principles which extend far beyond OLS to general
nonlinear setups. The most fundamental results are for extremum estimators for