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Bidding for Industrial Plants:
Does Winning a Million Dollar Plant Increase Welfare?
Michael Greenstone
MIT, American Bar Foundation and NBER
Enrico Moretti
University of California, Berkeley and NBER
November 2004
We thank Alberto Abadie, Michael Ash, Ha
Average Treatment Eects
Lecture for Economics 245A
Douglas G. Steigerwald
UC Santa Barbara
September 2011
Overview
goal: estimate impact of treatment
restriction: binary treatment
example: job training program
assume: observations are unconfounded
covaria
Identication and Estimation of Marginal Eects in Nonlinear
Panel Models
1
Victor Chernozhukov
Ivn Fernndez-Val
a
a
Jinyong Hahn
Whitney Newey
MIT
BU
UCLA
MIT
February 4, 2009
1
First version of May 2007. We thank J. Angrist, B. Graham, and seminar partici
Autocorrelation function
of the daily histogram time series
of SP500 intradaily returns
Gloria Gonzlez-Rivera
University of California, Riverside
Department of Economics
Riverside, CA 92521
Javier Arroyo
Universidad Complutense de Madrid
Department of Com
Autocontours: Dynamic Specication Testing
Gloria Gonzlez-Rivera
a
Zeynep Senyuz
Emre Yoldas
First version: January 2007
This version: October 2009
Abstract
We propose a new battery of dynamic specication tests for the joint hypothesis of
i.i.d.-ness and d
Do Local Economic Development Programs Work?
Evidence from the Federal Empowerment Zone Program
Matias Busso
University of Michigan
[email protected]
Patrick Kline
Yale University
[email protected]
Abstract
This paper evaluates the impact of Round I
Asymptotic Distribution of JIVE in a Heteroskedastic IV Regression with Many Instruments
John C. Chao, Department of Economics, University of Maryland, [email protected]
Norman R. Swanson, Department of Economics, Rutgers University, [email protected]
WNE
7/30/07
#5
Whats New in Econometrics
Lecture 5
Instrumental Variables with Treatment Eect
Heterogeneity: Local Average Treatment Eects
Guido Imbens
NBER Summer Institute, 2007
Outline
1. Introduction
2. Basics
3. Local Average Treatment Eects
4. Extra
Imbens/Wooldridge, Lecture Notes 5, Summer 07
Whats New in Econometrics
1
NBER, Summer 2007
Lecture 5, Monday, July 30th, 4.30-5.30pm
Instrumental Variables with Treatment Eect Heterogeneity:
Local Average Treatment Eects
1. Introduction
Here we investiga
Funding Sources
NSF Graduate Research Fellowships
Requirement: Permanent Resident or Citizen
www.fastlane.nsf.gov
Deadline: November 7 (2002)
Social Science Research Council
www.ssrc.org
Deadline: February (2003 - tentative)
Note: Designed for study outsi
1. Economics 245A: Cluster Sampling & Matching
(This document was created using the AMS Proceedings Article
shell document.)
Cluster sampling arises in a number of contexts. For example, consider a study of retirement saving. It is likely the case that re
Imbens/Wooldridge, Lecture Notes 1, Summer 07
Whats New in Econometrics
1
NBER, Summer 2007
Lecture 1, Monday, July 30th, 9.00-10.30am
Estimation of Average Treatment Eects Under Unconfoundedness
1. Introduction
In this lecture we look at several methods
Economics 245A
Introduction to Measure Theory
The goal of this lecture is to take the axioms of probability, which are introduced as the basis for statistical theory, and relate them to measure theory.
Probability
Probability is a subject that can be stud
NBER WORKING PAPER SERIES
BETTER LATE THAN NOTHING:
SOME COMMENTS ON DEATON (2009) AND HECKMAN AND URZUA (2009)
Guido W. Imbens
Working Paper 14896
http:/www.nber.org/papers/w14896
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge,
Economics 245A
Notes for Measure Theory Lecture
Axiomatic Approach
The axiomatic approach introduced by Kolmogorov starts with a set of axioms, as
do all axiomatic approaches, that are taken to be obvious. The axioms must be: (1)
complete, that is all the
Bayesian Inference
Lecture for Economics 245
Douglas G. Steigerwald
UC Santa Barbara
February 2011
Condence Intervals
Frequentist Inelegance
B an estimator of
P (
1.96
2 = Var (B )
B
+ 1.96) = .95
b
+ 1.96) = either 0 or 1
b an estimate of
P (
1.96
we
Imbens/Wooldridge, Lecture Notes 7, NBER, Summer 07
Whats New in Econometrics
1
NBER, Summer 2007
Lecture 7, Tuesday, July 31th, 11.00-12.30pm
Bayesian Inference
1. Introduction
In this lecture we look at Bayesian inference. Although in the statistics lit
Convergence of MCMC Algorithms in Finite Samples
Anna Kormilitsina and Denis Nekipelov
SMU and UC Berkeley
September 2009
Kormilitsina, Nekipelov
Divergence of MCMC
September 2009
Introduction
Motivation
MCMC widely used Bayesian method in frequentist con
WNE
7/30/07
#1
Whats New in Econometrics
Lecture 1
Estimation of Average Treatment Eects
Under Unconfoundedness
Guido Imbens
NBER Summer Institute, 2007
Outline
1. Introduction
2. Potential Outcomes
3. Estimands and Identication
4. Estimation and Inferenc
Maximum Likelihood Estimation
To determine the adequacy of an estimator, we have discussed the mean-square
error (MSE) criterion. As you have found from problems and it is not
always possible to nd an estimator that minimizes the MSE criterion. While the
WNE
7/30/07
#2
Whats New in Econometrics?
Lecture 2
Linear Panel Data Models
Jeff Wooldridge
NBER Summer Institute, 2007
1. Overview of the Basic Model
2. New Insights Into Old Estimators
3. Behavior of Estimators without Strict Exogeneity
4. IV Estimatio
Imbens/Wooldridge, Lecture Notes 2, Summer 07
Whats New in Econometrics?
NBER, Summer 2007
Lecture 2, Monday, July 30th, 11.00-12.30 am
Linear Panel Data Models
These notes cover some recent topics in linear panel data models. They begin with a
modern tre
NBER WORKING PAPER SERIES
COMPARING IV WITH STRUCTURAL MODELS:
WHAT SIMPLE IV CAN AND CANNOT IDENTIFY
James J. Heckman
Sergio Urzua
Working Paper 14706
http:/www.nber.org/papers/w14706
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambrid
NBER WORKING PAPER SERIES
LONG-TERM CONSEQUENCES OF VIETNAM-ERA CONSCRIPTION:
SCHOOLING, EXPERIENCE, AND EARNINGS
Joshua D. Angrist
Stacey H. Chen
Working Paper 13411
http:/www.nber.org/papers/w13411
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts
Identication and estimation of irregular correlated random
coecient models1
Bryan S. Graham and James L. Powell
Initial Draft: December 2007
This Draft: July 3, 2008
Preliminary and Incomplete
Abstract
In this paper we study identication and estimation of
NBER Summer Institute
Whats New in Econometrics: Time Series
Lecture 7
July 15, 2008
Recent Developments in Structural VAR Modeling
Revised July 23, 2008
7-1
Outline
1) VARs, SVARs, and the Identification Problem
2) Identification by Short Run Restriction
Inference for VARs Identied with Sign
Restrictions
Hyungsik Roger Moon
Frank Schorfheide
University of Southern California
University of Pennsylvania, CEPR, NBER
Eleonora Granziera
Mihye Lee
University of Southern California
University of Southern Califor
INVERSE PROBABILITY WEIGHTED ESTIMATION FOR
GENERAL MISSING DATA PROBLEMS
Jeffrey M. Wooldridge
Department of Economics
Michigan State University
East Lansing, MI 48824-1038
(517) 353-5972
[email protected]
This version: April 2003
1
ABSTRACT
I study inver