ECONOMETRICS I
1
Preface
1.1
Welcome
1.2
Context
I Overview
2
What is Econometrics
3
This Course
3.1
Virtual Campus
3.1.1
Contents
3.1.2
Calendar
3.1.3
Grading
3.1.4
Course Format
3.2
Bibliography
3.2.1
Basic bibliography
3.2.2
Recommended bibliography
3.2.3
Software
3.3
Gretl
Ingredients
Recommendations
4
Design of Experiments
5
Causal Models
5.1
An Example
5.2
Regression Analysis
6
Case 01: Monet
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
Case 02: Mobile
7.1
Data
7.2
Describing Data
7.3
Price by OS
7.3.1
Data
7.3.2
Research question
7.3.3
Preparing Data
7.3.4
Mean Comparison
7.3.5
More on data visualization
7.4
Price by Brand
7.5
Price by Screen Size
7.6
Price by Storage Capacity
7.7
Price by Dual Sim
II Design of Experiments
8
Introduction
8.1
An Example
9
Hypothesis Testing
9.1
How to?
9.2
Terminology
9.3
A snapshot
10
Inference on the Mean
10.1
One-Sample: Hypothesis Testing on the Mean
10.1.1
Example
10.2
Two-Samples: Hypothesis Testing on the Difference in Means
10.2.1
Independent samples and Equal variances
10.2.2
Example
10.3
p-values: t Distribution
III Causal Models
11
What are causal models?
12
Simple Linear Regression
12.1
Straigh Line Relationship
12.2
Topics to cover
12.2.1
Our Monet Case
12.3
Regression Basics
12.4
Calculating the Regression Line
12.4.1
Technical Note: the “Best Fitting Line”
12.5
Hypothesis Testing on Parameters
12.6
Confidence Intervals
12.6.1
Confidence Interval on Regression Coefficients
12.6.2
Confidence Interval on Fitted Values
12.7
Coefficient of Determination
12.7.1
Technical notes
12.8
Dummy Variables
12.8.1
A Dummy variable
12.8.2
In the Model
12.8.3
An Example
12.9
Log-Log Models
12.10
Quadratic Models
12.10.1
Example
12.11
Parameter Interpretation
12.12
Spurious Regression
13
Multiple Linear Regression
13.1
Applications
13.2
Model Parameters
13.3
Fitted Values and Residuals
13.4
ANOVA
13.5
R-squared, and Adjusted R-squared
13.6
Significance Testing of Each Variable
13.7
Assumptions of Multiple Linear Regression
13.8
Multicollinearity
13.8.1
The problem
13.8.2
Exact collinearity
13.8.3
Indicators of Multicollinearity
13.8.4
Detecting Multicollinearity
13.8.5
Corrections for Multicollinearity
13.8.6
Our Monet Case
13.8.7
Revisiting Monet Case
13.9
Heteroscedasticity
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
Datasets
D.1
Case 1
D.2
Case 2
D.3
Case 3
D.4
Case 4
D.5
Case 5
D.6
Case 6
D.7
Case 7
D.8
Case 8
D.9
Case 9
E
About me
Universidad Nebrija.
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Econometrics I | Class Notes
10
Inference on the Mean