ECONOMETRICS I
1
Preface
1.1
Welcome
1.2
Context
I Overview
2
What is Econometrics
3
This Course
Ingredients
Recommendations
3.1
Contents
3.1.1
Calendar
3.2
Syllabus
3.2.1
Grading
3.2.2
Course Format
3.3
Bibliography
3.3.1
Basic bibliography
3.3.2
Recommended bibliography
3.4
Tools
4
Design of Experiments
5
Causal Models
5.1
An Example
5.2
Regression Analysis
6
Basic Concepts: An Example
6.1
Dataset
6.1.1
What Are
Variables
?
6.2
Descriptive Statistics
6.2.1
Data Summary and Presentation
6.3
Data Display
6.4
The Output
6.4.1
Distribution I
6.4.2
Distribution II
6.5
New variables
6.5.1
Relation with PRICE
6.6
Comparing Group Means
6.6.1
Decision Making for Single Sample
6.6.2
Decision Making for Two Samples
7
Team Exercises
7.1
Homework: It’s your turn!
7.2
Data sets
7.2.1
Teacher Ratings
7.2.2
Smiles and Leniency
7.2.3
Credit Card Expenditure
7.2.4
Economics Journal Subscription
8
Just Another Example
8.1
Data
8.2
Describing Data
8.3
Price by OS
8.3.1
Data
8.3.2
Research question
8.3.3
Preparing Data
8.3.4
Mean Comparison
8.3.5
More on data visualization
8.4
Price by Brand
8.5
Price by Screen Size
8.6
Price by Storage Capacity
8.7
Price by Dual Sim
II Design of Experiments
9
What is experimental design?
10
What are the uses of DOE?
10.1
Components of DoE
11
What are the steps in DOE?
11.1
Nine Basic Rules
11.2
Uses of DoE
Useful Links
12
Hypothesis Testing
12.1
How to?
12.2
Terminology
12.3
A snapshot
13
Inference on the Mean
13.1
One-Sample: Hypothesis Testing on the Mean
13.1.1
Example
13.2
Two-Samples: Hypothesis Testing on the Difference in Means
13.2.1
Independent samples and Equal variances
13.2.2
Example
13.3
Working with Excel
13.3.1
Desktop version
13.3.2
Cloud version
13.4
p-values: t Distribution
14
Inference on Proportions
14.1
One-Sample: Hypothesis Testing on a Proportion
14.1.1
The Hypotheses and
\(p\)
-value
14.1.2
Decision rule
14.1.3
Example 01
14.1.4
Example 02
14.1.5
Example 03
14.2.4
Example 02
14.3
Standard Normal Distribution
III Causal Models
15
What are causal models?
16
Simple Linear Regression
16.1
Straigh Line Relationship
16.2
Topics to cover
16.2.1
Our Monet Case
16.3
Regression Basics
16.4
Calculating the Regression Line
16.4.1
Technical Note: the “Best Fitting Line”
16.5
Hypothesis Testing on Parameters
16.6
Confidence Intervals
16.6.1
Confidence Interval on Regression Coefficients
16.6.2
Confidence Interval on Fitted Values
16.7
Coefficient of Determination
16.7.1
Technical notes
16.8
Dummy Variables
16.8.1
A Dummy variable
16.8.2
In the Model
16.8.3
An Example
16.9
Log-Log Models
16.10
Quadratic Models
16.10.1
Example
16.11
Parameter Interpretation
16.12
Spurious Regression
17
Multiple Linear Regression
17.1
Model Parameters
17.2
Fitted Values and Residuals
17.3
ANOVA
17.4
R-squared, and Adjusted R-squared
17.5
Significance Testing of Each Variable
17.6
Assumptions of Multiple Linear Regression
17.7
Multicollinearity
17.7.1
The problem
17.7.2
Exact collinearity
17.7.3
Indicators of Multicollinearity
17.7.4
Detecting Multicollinearity
17.7.5
Corrections for Multicollinearity
17.7.6
Our Monet Case
17.7.7
Revisiting Monet Case
17.8
Heteroscedasticity
IV Final Project
18
Intro
19
Case A: Body fat in women
20
Case B: Lung Function in 6 to 10 Year Old Children
21
Peru
21.1
Activity
Appendix
A
Descriptive Statistics
B
In Excel
C
Students’t Distribution
C.1
Degrees of Freedom (df)
C.2
Area under the curve
C.3
The t-table
C.4
Acceptance/Rejection Region
D
Team Exercises
D.1
Data sets
D.1.1
Hospital Infection Risk
D.1.2
Skin Cancer Mortality
D.1.3
Hand and Height
D.1.4
Old Faithful geyser
D.1.5
Real State
D.1.6
Teen Birth Rate and Poverty Level Data
D.1.7
Lung Function in 6 to 10 Year Old Children
E
Time to Play
E.1
Manual
F
About me
Universidad Nebrija.
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Econometrics I | Class Notes
13
Inference on the Mean