# Introductory Econometrics Course Outline

```STRATHMORE INSTITUTE OF MATHEMATICAL SCIENCES
BBS Financial Economics, BBS Financial Engineering &amp; BBS Actuarial Science
COURSE OUTLINE
Unit Code and Title: BSE 2103: Introductory Econometrics
Lecturers: Dr. Caroline Kariuki
Dr. Muthoni Ng’ang’a
Email: cwkariuki@strathmore.edu
Email: mnganga@strathmore.edu
Office: MSB basement staffroom/SIMS-Phase1
Purpose of the course:
To enable students to analyse economic data and establish relationships as well as forecast using
econometric methods.
Course Learning Outcomes:
After completing this course, the student should be able to:
1. Describe the nature of econometrics.
2. Discuss the assumptions that underpin the classical regression model.
3. Describe the fundamental techniques and applications of the simple and multiple regression
models.
4. Apply regression analysis to data sets in order to carry out hypothesis testing and predictions.
Contact Hours: 45
Prerequisite: Probability and Statistics I, Algebra and Pre-calculus
Lectures: Monday 1.15-4.15
Consultation Hours: Please make appointments via email.
1
Topic and content (sub-topics)
Intended Learning Outcomes
Activities
1: Introduction to Econometrics
a) Introduction to Econometrics
b) Methodology of Econometrics
c) Types of data
d) Causality and Association
Before class:
• Explain the meaning of Econometrics
• Distinguish between different types of Watch videos on eLearning:
data sets
i. What is econometrics
• Make a distinction between causality
ii. The danger of mixing up
and association
causality and correlation.
Class discussion
Homework
•
questions from Wooldridge Ch 1
(Problems 1-3).
After class Quiz:
•
Complete the Multiple Choice
Questions on eLearning before
moving on to Chapter 2.
R practical session:
•
Wooldridge Ch 1 Computer
Questions (C1, C2, C5,)
2. Simple Regression Model
a) Estimation and interpretation of
simple regression model
b) Population regression function and
the Sample regression function
• Define and estimate the simple Class discussion
regression model
After class:
• Distinguish between the population
• Watch videos on eLearning:
regression function and the sample
i) Linear Regression - Least
regression function
• Define the residual
Squares Criterion.
2
c) Sum of least squares; deriving the
Ordinary Least Squares Estimates i.e
the parameters 𝛽0 and 𝛽1
d) Algebraic properties of OLS
Statistics
e) Gauss-Markov assumptions that
must hold for OLS estimates to be
unbiased.
f) Total Sum of Squares, Explained
Sum of Squares and Residual Sum
of squares
g) Goodness of Fit
h) The meaning of the term ‘Linear’
i) Changing units of measurement of
the variables
j) Incorporating natural logarithms in
a simple regression
3. Multiple Regression Analysis
a) The model with two independent
variables and k independent
variables
b) Obtaining OLS estimates
c) Meaning of ‘holding other factors
fixed’
d) Goodness of Fit
e) Gauss-Markov assumptions
f) Over-specifying and
underspecifying a regression model
ii) Deriving OLS estimators
• Explain what is meant by sum of least
squares
iii) What is a biased estimator?
• Derive the Ordinary Least Squares
Homework
Estimates
• Describe the Properties of OLS • questions from Wooldridge Ch. 2
Statistics
(Problems 1,2, 3, 4, 5, 6, 11).
• Explain the Goodness of Fit for a model
After class Quiz:
• Define the term ‘Linear’
• Explain the effects of changing units of • Complete the Multiple Choice
measurement on OLS statistics
Questions on eLearning before
moving on to Chapter 3.
R practical session:
Wooldridge Ch. 2 Computer
Questions (C1 –C6)
• Discuss about a regression model with
k independent variables
• Explain how to interpret the OLS
multiple regression equation
• Compare and Contrast the simple and
multiple regression estimates
• Describe the Gauss-Markov
assumptions under which the OLS
estimates are unbiased
• Explain the issue of including irrelevant
variables or excluding relevant
variables in a regression model
3
Before class
•
Watch video on eLearning:
i) Perfect collinearity
Class discussion
After class
•
Watch videos on eLearning:
ii) Omitted variable bias
iii) Least squares estimators as
BLUE
g) Efficiency of OLS: The GaussMarkov Theorem
h) Language of the multiple regression
analysis
• Discuss the Homoskedasticity
assumption
• Discuss about the linear relationship
among independent variables
• Explain the importance of the GaussMarkov Theorem, which justifies the
use of OLS
iv) Heteroscedasticity summary
Homework
•
questions from Wooldridge Ch. 3
(Problems 1,2, 3, 4, 5, 7, 8).
After class Quiz:
•
Complete the Multiple Choice
Questions on eLearning before
moving on to Chapter 4.
R practical session:
•
Wooldridge Ch. 3 Computer
Questions ( C2, C3, C6)
4. Inference
a) Sampling Distributions of the OLS
Estimators
b) Hypothesis testing
✓ T-Test
✓ Confidence Intervals
✓ F- Test
• Discuss the normality assumption of
the error term
• Test Hypotheses about a Single
Population Parameter
• Construct and interpret confidence
intervals for population parameters
• Test Hypotheses about a single linear
combination of the parameters
• Test multiple linear restrictions
Before class:
Watch video on eLearning:
i.
What is a standard normal
distribution?
Class discussion
After class:
Watch videos on eLearning:
ii.
What is the Central Limit
Theorem?
iii.
What is a sampling
distribution?
4
Homework
•
questions from Wooldridge Ch. 4
(Problems 1,2, 3, 4, 5, 6, 8, 9,10,
11,12).
After class Quiz:
Complete the Multiple Choice
Questions on eLearning before
moving on to Chapter 5
R practical session:
•
Wooldridge Ch. 4 Computer
Questions ( C1, C3, C5)
R quiz – Covers all material from chapter 1-4
5. OLS Asymptotics
a) Consistency
b) Asymptotic Normality and Large
Sample Inference
c) Asymptotic Efficiency of OLS
• Discuss the Inconsistency in OLS
Class discussion
Homework
•
questions from Wooldridge Ch. 5
(Problems 2,3,4).
After class:
Watch video on eLearning:
i.
6. Further issues in Multiple Regression
Analysis
a) Effects of Data Scaling on OLS
Statistics
•
Unbiasedness and consistency
Discuss the effects of changing the units Class discussion
of measurement on OLS parameters
After class:
Watch video on eLearning:
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b) More on Functional Form
c) More on Goodness-of-Fit and
Selection of Regressors Prediction
and Residual Analysis
• Interpreting parameters in different
functional forms including logarithmic
• Evaluating regression models using
i.
Heteroscedasticity and log
transformation
Homework
questions from Wooldridge Ch 6
(Problems 1,3,4,6,7).
After class Quiz:
•
Complete the Multiple Choice
Questions on eLearning before
moving on to Chapter 7.
7. Heteroscedasticity
a) Consequences of Heteroskedasticity
for OLS
b) Computing HeteroskedasticityRobust LM Test
c) Heteroskedasticity Tests
✓ Detect heteroskedasticity
✓ Explain the consequences of
Heteroskedasticity
✓ Discuss and perform corrections
for heteroskedasticity
Class discussion
R practical session:
•
Computer Questions using R
After class:
Watch videos on eLearning:
o Park Test
o White Test
o Breusch-Pagan Test
d) Remedial measures of
Heteroskedasticity
i.
Weighted least squaresmathematical introduction
ii.
Weighted least squares- an
example
After class Quiz:
Complete the Multiple Choice
Questions on eLearning before
moving on to Chapter 8.
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8. Multicollinearity
a) Nature of multicollinearity
b) Consequences of multicollinearity
c) Detecting multicollinearity
d) Dealing with multicollinearity
•
Describe the problem and nature of
Class discussion
multicollinearity
Homework:
• Explain the practical consequences of
• questions from Studenmund
multicollinearity
Ch 8 (problems 1,2,3,5,6,10)
• Explain how to detect multicollinearity
• Discuss remedial measures can be taken
to alleviate the problem of
multicollinearity
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Type
Short quizzes (MCQs) in specified topic
Weighting (%)
30%
R quiz
10%
Examination
Total
60%
100 %
Recommended Text-books
1. Wooldridge, J. (2013). Introductory Econometrics: A Modern Approach, 5th Edition. Mason,
Cengage Learning. (We shall closely follow this text)
2. Studenmund, A. H. (2017). Using Econometrics: A Practical Guide, 7th Edition. Pearson.
3. Gujarati, D. N. (2004). Basic Econometrics, Fourth Edition. The McGraw−Hill Companies.
Classes
1. Punctuality is fundamental.
2. Active participation in class discussions is encouraged
3. You must complete all your assignments
4. Plagiarism is a serious offence.
5. R practice classes are compulsory
6. Notwithstanding the above, collaboration in homework assignments (NOT quizzes) is
encouraged as this promotes team spirit and group synergy, as long as originality is
preserved.
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