Applied Econometrics
1
Preface
1.1
About this
1.2
Update: April 23, 2019
1.3
Update: April 16, 2019
1.4
Acknowledgement (as of April 16, 2019)
2
Introduction to the course
2.1
What is econometrics?
2.2
Why do we need to learn computation
2.3
Why do we use R?
3
Introduction of
R
and
R studio
3.1
Getting Started
3.2
Helps
3.3
Quick tour of Rstudio
3.4
Basic Calculations
3.5
Getting Help
3.6
Installing Packages
4
Data and Programming
4.1
Data Types
4.2
Data Structures
4.3
Vectors
4.3.1
Basics of vectors
4.3.2
Useful functions for creating vectors
4.3.3
Subsetting
4.4
Vectorization
4.5
Logical Operators
4.6
Matrices
4.6.1
Basics
4.6.2
Matrix calculations
4.6.3
Getting information for matrix
4.7
Lists
4.8
Data Frames
4.9
Programming Basics -Control flow-
4.9.1
if/else
4.10
for
loop
4.11
Functions
5
Data frame
5.1
Introduction
5.2
Load csv file
5.3
Examine dataframe
5.4
Subsetting data
6
Exercise 1
6.1
Update (as of 10am, April 18th)
6.2
Question: Examine the law of large numbers through numerical simulations
6.2.1
How to implement
6.2.2
What to submit
7
A Review of Statistics
7.1
Estimation
7.1.1
Properties of the estimator
7.1.2
Sample mean
\(\bar{Y}\)
is unbiased and consistent
7.2
Hypothesis Testing
7.2.1
Central limit theorem
7.2.2
Hypothesis testing
8
Linear Regression 1: Theory
8.1
Regression framework
8.2
Theoretical Properties of OLS estimator
8.3
Interpretation and Specifications of Linear Regression Model
8.3.1
Nonlinear term
8.3.2
log specification
8.3.3
Dummy variable
8.3.4
Interaction term
8.4
Measures of Fit
8.5
Statistical Inference
8.5.1
Distribution of the OLS estimators based on asymptotic theory
8.5.2
Hypothesis testing
8.5.3
Confidence interval
8.5.4
Homoskedasticity vs Heteroskedasticity
9
Linear Regression 2: Implementation in R
9.1
Implementation in R
9.1.1
Preliminary: packages
9.1.2
Empirical setting: Data from California School
9.1.3
Step 1: Descriptive analysis
9.1.4
Step 2: Run regression
10
Linear Regression 3: Discussions on OLS Assumptions
10.1
Introduction
10.2
Endogeneity problem
10.2.1
Omitted variable bias
10.2.2
Correlation v.s. Causality
10.3
Multicollinearity issue
10.3.1
Perfect Multicollinearity
10.3.2
Imperfect multicollinearity.
10.4
Lesson for an empirical analysis
11
Exercise 2 (Problem Set 3)
11.1
Rules
11.2
Question 1: Omitted Variable Bias
11.3
Question 2: Empirical Analysis using Data from Washington(2008, AER)
11.3.1
Preliminary: data cleaning
11.3.2
Questions
12
Instrumental Variable 1: Framework
12.1
Introduction: Endogeneity Problem and its Solution
12.2
Examples of Endogeneity Problem
12.2.1
More on Omitted Variable Bias
12.2.2
Measurement error
12.2.3
Simultaneity (or reverse causality)
12.3
Idea of IV Regression
12.4
Formal Framework and Estimation
12.4.1
Model
12.4.2
Estimation by Two Stage Least Squares (2SLS)
12.4.3
Conditions for Valid IVs in a general framework
12.5
Check Instrument Validity
12.5.1
Relevance
13
Instrumental Variable 2: Implementation in R
13.1
Example 1: Wage regression
13.1.1
Discussion on IV
13.2
Example 2: Estimation of the Demand for Cigaretts
13.3
Example 3: Effects of Turnout on Partisan Voting
14
Exercise 3 (Problem Set 4)
14.1
Rules
14.2
Question: Demand Estimation
14.2.1
Questions
15
Exercise 4 (Problem Set 5)
15.1
Rules
15.2
Question 1: Hansford and Gomez (2010, APSR)
15.3
Questions
16
Panel Data
16.1
Contents
16.2
Introduction
16.3
Overview
16.4
Framework
16.5
Estimation (within transformation)
16.5.1
Importance of within variation
16.6
FE, FE, and FE
16.7
Panel + IV
16.8
Standard Errors
17
Panel Data 2: Implementation in R
17.1
Preliminary:
17.2
Panel Data Regression
17.3
Panel Data with Instrumental Variables
17.4
Some tips in
felm
command
17.4.1
How to report heteroskedasticity robust standard error in
stargazer
17.4.2
How to conduct F test after
felm
18
Introduction to Causal Inference
18.1
Introduction:
19
Discrete Choice Model
19.1
Introduction:
20
Difference in Differences
20.1
Reference
20.2
Introduction:
21
Regression Discontinuity Design
21.1
Introduction:
Published with bookdown
Lecture Note for Applied Econometrics
19
Discrete Choice Model
19.1
Introduction:
Discrete choise (logit, profit)
Discrete choice example (from Text)
Group logit, or Berry (1994)’s logit
For the multinomial choice, see my lecture note or Train.