Australian School of Business
School of Banking and Finance
RMF2
Midsession exam 2010
Robert Kohn
Exam Duration: 2 hours.
Reading Time: 15 minutes.
The exam is out of 77 marks.
Attempt as many questions as you can.
In answering questions, be precise
Australian School of Business
School of Banking and Finance
RMF2
Midsession exam 2010
Robert Kohn
Exam Duration: 2 hours.
Reading Time: 15 minutes.
The exam is out of 77 marks.
Attempt as many questions as you can.
In answering questions, be precise
Midterm Exam
RMF2 2008
Robert Kohn
Exam Time: 1 hour and 30 minutes.
Reading time: 10 minutes.
Please attempt 3 out of the 4 questions. All questions are of equal worth.
You can bring in a sheet of paper written on both sides.
You are expected to have you
Australian School of Business
School of Banking and Finance
RMF2
Problem Set 1
Robert Kohn
This problem is for class and tutorial discussion and is not to be handed
in. These will be discussed in Week 3 of the course. All data sets for this
problem are on
Problem Set 2 Sample Solution
Question 1
log using "E:\Work\Tutoring\RMF2\probset2"
insheet using "E:\Work\Tutoring\RMF2\dataset\rmf2_dataset1(2).csv"\
summarize
gen t=_n / set t equals to number of current observations/
tsset t / declare data to be time
Research Method 2
Problem Set 1 Solutions
Question 2
Create log file (record session), import data and summarize data
log using "D:\Work\Tutoring\RMF2\dataset\probset1", replace
insheet using "D:\Work\Tutoring\RMF2\dataset\anscombe.csv", clear
summarize
o
Faculty of Commerce and Economics
School of Banking and Finance
RMF2
Problem Set 5
Robert Kohn
May 10 2010
Please hand this problem set in by 5:00 pm May 20. You can work singly
or in groups of 2 or 3.
1. Suppose we have n independent observations yi N (0
Faculty of Commerce and Economics
School of Banking and Finance
RMF2
Problem Set 3
Robert Kohn
March 29 2010
Questions 1 to 2 are to be treated as reports. For each of questions 1 to 2,
Do an executive summary outlining the problem and a short descriptio
Faculty of Commerce and Economics
School of Banking and Finance
RMF2
Problem Set 2 Solutions
Robert Kohn
March 2010
1. See separate le.
2. Read through chapter 2 of Stock and Watson.
(a) Suppose that Y = a + bX + e where e N (0, 2 ) and X and e
are indepe
Faculty of Commerce and Economics
School of Banking and Finance
RMF2
Problem Set 2
Robert Kohn
March 2010
This problem is for class and tutorial discussion and is not to be handed
in. These will be discussed in Week 4 of the course. All data sets for this
Midterm Exam
RMF2 2009
Robert Kohn
Exam Time: 1 hour and 30 minutes.
Reading time: 10 minutes.
Please attempt all questions.
You can bring in a sheet of paper written on one side only.
You are expected to have your own calculator.
Please write in the exam
Assignment 1:
Problems Set 3
By
Robert Roszkowski,
Ashley Gottlieb
Nathan Moldovan
S1, 2010
Fins4779
Assignment 1: Problems Set 3
Fins4779
Question 1
Executive Summary
In this report we analyze both worker and pocket efficiencies in an attempt to optimize
Australian School of Business
School of Banking and Finance
FINS 4779 /5579
RESEARCH METHODS IN FINANCE 2
ROBERT KOHN
COURSE OUTLINE
SEMESTER 1, 2010
TABLE OF CONTENTS
1. STAFF CONTACT DETAILS
1
2. COURSE DETAILS
1
2.1 Teaching Times and Locations
2.2 Uni
Chapter 2. Linear Regression continued
Author: Robert Kohn
Australian School of Business
University of New South Wales
School of Banking and Finance
Summary
This chapter continues the review of the linear regression
model.
We consider the eect of leverage
Chapter 2. Linear Regression continued
Author: Robert Kohn
Australian School of Business
University of New South Wales
School of Banking and Finance
Summary
This chapter continues the review of the linear regression model.
We consider the eect of leve
Chapter 1. Review of linear Regression.
Robert Kohn
Australian School of Business
University of New South Wales
School of Banking and Finance
Abstract
This chapter reviews some of the concepts of linear
regression. We start with an example so that we can
CHAPTER 1. REVIEW OF LINEAR REGRESSION.
ROBERT KOHN
Diagnostics; Leverage ; Normal probability plot; Normal quantile
plot; Outliers; Standard error;
Abstract. This chapter reviews some of the concepts of linear
regression.
We start with an example so that
Chapter 7: Endogeneity, instrumental
variables and simultaneous equations
Robert Kohn
May 23 2010
1
Outline
Contents
1
Introduction
Outline of chapter Outline
This chapter deals with endogeneity
instrumental variables
simultaneous equations models
2
En
Chapter 7: Endogeneity, instrumental variables and simultaneous equations
Chapter 7: Endogeneity, instrumental
variables and simultaneous equations
Robert Kohn
Australian School of Business
University of New South Wales
RMF2 2010
May 23 2010
Chapter 7: En
Bootstrapping the correlation coecient
Robert Kohn
May 15 2010
1
Outline
Contents
1 Introduction
1
2 Bootstrapping the correlation coecient
1
3 Asset allocation, the multivariate normal distribution and
the bootstrap
18
4 Nonparametric Bootstrap for asset
Multivariate normal distribution and
multivariate regression
Robert Kohn
may 11 2010
1
Outline
Contents
1 Introduction
1
2 Covariance matrix and sample covariance matrix
7
1
Introduction
Further discussion of logistic regression
Generalized linear models
Chapter 5: Binary Regression
Chapter 5: Binary Regression
Robert Kohn
Australian School of Business
University of New South Wales
RMF2 2010
April 10 2010
Chapter 5: Binary Regression
Outline
Chapter 5: Binary Regression
Introduction
Introduction
This chap
Outline
Contents
1 Introduction
1
2 BIC criterion
7
3 Logistic regression model
11
4 Analysis of Mroz data
18
1
Introduction
Introduction
This chapter looks at regression models with a binary dependent vari-
able. The ideas in this chapter are also discu
Chapter 3. Maximum likelihood estimation of the
regression model and small and large sample
inference for the regression model.
Robert Kohn, Australian School of Business
RMF2 2010
Summary
This chapter considers maximum likelihood estimation of
the regres
Chapter 3: Maximum likelihood estimation of
the regression model
Small sample and large sample inference.
Robert Kohn
March 28 2010
March 29, 2010
1
Summary
This chapter considers maximum likelihood estimation of the regression
model
The small and large
Chapter 2C. Transforming the dependent variable and Heteroscedasticity. Robert Kohn, Australian School of Business RMF2 2010
Summary
This chapter considers how transformation can overcome heteroscedasticity.
Considerations when doing point prediction.
Log