API-202A Spring 2009 Assignment #1: Review of Statistics and Intro to Stata Due February 5th at 5pm (Drop in API202A mailbox inside manila envelope with your KSG mailbox # written outside) The purpose of this exercise is to help you review some key c

API-202 Spring 2009 Assignment #1: Review of Statistics and Intro to Stata Suggested Solutions The purpose of this exercise is to help you review some key concepts from API-201 and to become familiar with Stata. The assignment has 2 parts: Part I doe

Empirical Methods II (API-202A)
Kennedy School of Government Harvard University
Lecture Notes 6 Non-linear Relationships Interactive Terms
Non-linear regression can take on more than one form: 1. Logs and Quadratics (last class) The predicted cha

API-202A Empirical Methods II Spring 2009 Assignment 2 Suggested Solutions
Section I: Smoking and Cancer (The answer below is more complete than what you were asked for.)
Xi
1 (Holland) 2 (Finland) 3 (GB) 4 (Canada) 5 (Norway) (.) (. ) / N
460 1115

API-202 Empirical Methods II Spring 2009 A SHORT INTRODUCTION TO STATA 9.0 Course materials and data sets will assume that you are using Stata to complete the analysis. Stata is available on all of the computers in the Kennedy Schools computer lab. H

Harvard ID Number: _
API-202BCD Spring 2006 Quiz Answer Key
1.
a. (2 points) In the regression results, which are reproduced below, circle and label 0 and 1 .
-| Robust bwghtlbs | Coef. Std. Err. -+-cigs | -.0321108 .0054833 _cons | 7.485744 .035

Empirical Methods II (API-202A)
Kennedy School of Government Harvard University
Lecture Notes 7 OLS Standard Errors and F-test
I OLS Standard Errors SE( 1 )
Refresher: The need for hypothesis testing Regressions: estimate partial association i

Empirical Methods II (API-202A)
Kennedy School of Government Harvard University
Lecture Notes 8
Binary Dependent Variables
I INTRODUCTION o Up to now: use binary variables (dummies) as RHS variables in a regression. o This LN: explore the use of b

Empirical Methods II (API-202)
Kennedy School of Government Harvard University
Lecture Notes 10 Fixed Effects
I INTRODUCTION Last Class: Introduced the concept of fixed effects in the context of the STAR Experiment o Be sure to revisit the last s

Empirical Methods II (API-202)
Kennedy School of Government Harvard University
Lecture Notes 11 Difference in Differences
So far we have seen three ways of dealing with OVB: o Include omitted explanatory variables o Randomized experiments o Fixe

Empirical Methods II (API-202)
Kennedy School of Government Harvard University
Lecture Notes 12 Overview of Data Issues and Forecasting
Today: An overview of how to think about and deal with: o Data issues that you might encounter when working wit

Empirical Methods II (API-202)
Kennedy School of Government Harvard University
Lecture Notes 13 Instrumental Variables
I - INTRODUCTION Three important threats to internal validity (causal identification): 1) Omitted variable bias: a. Certain varia

Harvard ID Number: _ Name: _
API-202BCD Spring 2005 Quiz 1. Please put your ID number at the top of each page. On this page only, also include your name. 2. You may use a calculator. You may not use any other materials. 3. You have 20 minutes to com

Empirical Methods II (API-202A)
Kennedy School of Government Harvard University
Lecture Notes 5 Non-linear Relationships Quadratic and Log Forms
I INTRODUCTION So far we have been modeling the relationship between variables as a linear relations

Empirical Methods II (API-202)
Kennedy School of Government Harvard University
Lecture Notes 9 Randomized Experiments (and Intro to Fixed Effects)
I INTRODUCTION Today: Analyze validity of randomized experiments and introduce the concept of fixed

API-202A Empirical Methods II Spring 2009 Assignment 3
Section I due February 24 at 10:10am in Classroom (no manila envelope needed) Section II due February 26 at 10:10am in Classroom (no manila envelope needed) Sections III and IV due February 26 at

API-202 A Spring 2009 TUTORIAL FOR STATA This tutorial will help you prepare for Part 2 of Assignment 1, and also for using Stata throughout this course. You do not need to submit any output from the tutorial. Please note that this tutorial is a comp

HARVARD UNIVERSITY THE JOHN F. KENNEDY SCHOOL OF GOVERNMENT Course Syllabus for API-202A Empirical Methods II Spring 2009 Professor: Herman Bennett Office: Littauer 231, Herman_Bennett@ksg.harvard.edu Office Hours: Tuesdays, 4:30pm-6:30pm. Assistant:

API-202A Empirical Methods II Spring 2009 Assignment 2
Due February 19th at 5pm (Drop in API202A mailbox inside manila envelope with your KSG mailbox # written outside)
Section I: Smoking and Cancer
Few medical professionals doubt that smoking leads

API-202: Sections A Empirical Methods II Spring 2009 Assignment 4
Due March 6 at 5pm (Drop in API202A mailbox inside manila envelope with your KSG mailbox # written outside)
Distance to College and Years of Schooling The University of California is

Appendix A - THE STATE TRAFFIC FATALITY DATA SET (from Stock and Watson)
The data are for the lower 48 U.S. states (excluding Alaska and Hawaii), annually for 1982 through 1988. Traffic fatality data were obtained from the U.S. Department of Transpor

Empirical Methods II (API-202A)
Harvard Kennedy School of Government
API-202 Section A Spring 2009 COURSE OVERVIEW
Main goal of the course: To develop your skills to critically assess policy studies and to give you the potential to participate in

Lecture Note 1 Introduction to Regression Analysis Empirical Methods II (API202A) Spring 2009 Harvard Kennedy School
1
1.1
How Can Regression Analysis Help Us Shape Policy?
Denition
Regression analysis (RA) is a widespread statistical tool used t

Lecture Note 2 Mechanics and Interpretation of OLS Empirical Methods II (API202A) Spring 2009 Harvard Kennedy School
1
How Do We Compute the Partial Association We Observe in the Data i.e. the s?
Recall our SEQ:
y = 0 + 1 x1 + 2 x2 + . + K xK +

Empirical Methods II (API-202)
Kennedy School of Government Harvard University
LECTURE NOTES 3 - REGRESSION WITH DUMMY VARIABLES
I - INTRODUCTION Qualitative information can sometimes be captured by defining a binary variable (i.e. a zero-one var

Lecture Note 4 Under What Conditions Can We Infer Causality from OLS? Empirical Methods II (API202A) Spring 2009 Harvard Kennedy School
We have studied so far: The concept of causality (our goal) and how we represent it with the SEQ (1 ) The conc