Financial Econometrics
Preliminaries
Lecturer: Dr. Cal Muckley
Initial Comments
There are 12 Financial Econometrics lectures this
semester see course outline for a detailed schedule.
Attendance at lectures is optional.
Please keep talking to a minimum
_
November 3rd 2016
ECON 30130 Midterm Exam
Prof. Paul Devereux
Time Allowed: 50 Minutes
Select the one option you believe is the best answer.
Marking scheme: Correct answer = +1
No answer = 0
Wrong answer = 0
There is no negative marking so answer all qu
ECON 30130
Econometrics
2016/17
Sample MCQ Questions
1. The term u in an econometric model is usually referred to as the
a. error term
b. parameter
c. hypothesis
d. dependent variable
e. residual
2. Suppose x2 has been omitted from the regression equation
ECON 30130
Econometrics
2016/17
Practice Problems on Multiple Regression (answers not to be turned in)
Question 1
Consider the multiple regression model with three independent variables, under the classical
linear model assumptions MLR.1 through MLR.6:
y
ECON 30130
Econometrics
2016/17
Practice Problems on Multiple Regression (answers not to be turned in)
Question 1
A dataset on working men was used to estimate the following equation:
educ 10.36 .094sibs .131meduc .210 feduc
n 722, R 2 .214
Where educ is
ECON 30130
2016/17
Econometrics
Practice Problems on Simple Regression (answers not to be turned in)
Question 1
Suppose that you are asked to evaluate whether more job training makes workers more
productive. However, rather than having data on individual
SEMESTER ONE EXAMINATIONS
ACADEMIC YEAR 2016/2017
ECON30130
Econometrics
SAMPLE FINAL EXAM
Professor Barry Reilly
Professor Karl Whelan
Professor Paul Devereux*
Time Allowed: 2 Hours
Instructions for Candidates
Please answer all questions. Question 1 carr
EXOGENEITY
Two Stage Least Squares
and Instrumental Variables
Estimation
Lecturer: Dr. Cal Muckley
Overview [1 of 4]
The exogeneity assumption of the Gauss-Markov theorem: explanatory
variables are either (i) non-stochastic, i.e., fixed in repeated sample
AN OVERVIEW OF THE
CLASSICAL LINEAR
REGRESSION MODEL
Lecturer: Dr. Cal Muckley
Overview of Handout [1 of 2]
Regression & OLS
Regression Basics; Correlation; PRF and SRF
+ an example: the CAPM
The OLS Estimator and its Derivation
Gauss Markov theorem: O
Relaxing the Assumptions of
the Classical Linear Regression
Model
Multicollinearity
Lecturer: Dr. Cal Muckley
Overview of Handout
This Handout addresses multicollinearity under the following headings:
[1] its Nature; [2] Consequences; [3] Detection and [4
MLEs and Qualitative Response
Regression Models
Lecturer: Dr. C. Muckley
Overview [1 of 3]
MLE is a standard method for fitting the parameters of a density function.
It consists of estimating the unknown parameters such that the probability of observing
t
CLRM APPLICATIONS
Lecturer: Dr. Cal Muckley
Overview of Handout
Several Applications building on CAPM example
Demand function for rental properties
Jensens alpha portfolio performance
Overreaction Hypothesis losers subsequently
outperform
A Regression
Non-Linear Regression Models
Lecturer: Dr. Cal Muckley
Overview I
Intrinsically non-linear in-the-parameters regression
models (NLRMs) cannot be transformed such that they are
linear in-the-parameters. Nonetheless, they are
estimable.
In particular, NLR
Relaxing the Assumptions of the
Classical Linear Regression Model
Model Specification &
Diagnostic Checking
Lecturer : Dr. Cal Muckley
Module : Financial Econometrics
Overview of the Handout [1]
The CLRM assumes that the adopted model is correctly specif
Relaxing the Assumptions of the Classical
Linear Regression Model
Autocorrelation
Lecturer : Dr. Cal Muckley
Module : Financial Econometrics
Overview of Handout[1]
In this handout the topic of autocorrelation is addressed under the
following main headings
Relaxing the Assumptions of the
Classical Linear Regression Model
Heteroskedasticity
Lecturer :
Module :
Cal Muckley
Financial Econometrics
Overview [1]
In this handout the topic of heteroskedasticity is addressed under
the following main headings: [1] na
ECON 30130
2016/17
Econometrics
Practice Problems on Multiple Regression (answers not to be turned in)
Question 1
Suppose you collect data from a survey on wages, education, experience, and gender. In
addition, you ask for information about marijuana usag