LU7
Estimation Using
EViews
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Data (Excel)
Estimation using OLS (EViews)
Interpretation
2
What to do now?
Prepare the data (most preferable in excel format).
How?
Key in all the data collected from the trustable
sources.
Check on the da

Fakulti Ekonomi Dan Perniagaan
Universiti Malaysia Sarawak
Econometrics Tutorial 7 / Semester 2 (2014/2015)
Please submit handwritten answer before tutorial class
start.
Question 1
a) How do you define Granger Causality?
b) What are the four possibilities

LU 3
Linear Regression
Model
1
Learning Objectives
By the end of this lecture you will:
understand the simple linear regression model
understand the logic behind the method of
ordinary least squares (OLS) estimation
The following notation will be used

LU9
Autocorrelation or
Serial Correlation
4 Basic Questions
What is the nature of autocorrelation?
What are the consequences of autocorrelation?
How to detect autocorrelation?
What are the remedy measures to autocorrelation?
2
Nature of Autocorrelatio

Fakulti Ekonomi Dan Perniagaan
Universiti Malaysia Sarawak
Econometrics Tutorial 5 / Semester 2 (2014/2015)
Please submit handwritten answer before tutorial class
start.
Question 1
a) What is meant by autocorrelation?
b) Why is autocorrelation a problem?

LU8
Functional Form
and Logarithm
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Functional Form
Logarithms in Econometrics
2
Review
Regression with Constant Term
So far we have only considered linear regression model
with a constant term in it.
Usually, the intercept has no econom

Fakulti Ekonomi Dan Perniagaan
Universiti Malaysia Sarawak
Econometrics (EBQ2074)
Tutorial 3 / Semester 2 (2014/2015)
Submit handwritten answer before the tutorial starts
Question 1
a) Write the equation of the multiple regression linear model for the cas

Fakulti Ekonomi Dan Perniagaan
Universiti Malaysia Sarawak
Econometrics (EBQ2074)
Tutorial 4 / Semester 2 (2014/2015)
Submit handwritten answer before the tutorial starts
Question 1
a) Write the equation of the multiple regression linear model for the cas

LU 2
Review
Some Basic
Statistical Concepts
Population, Sample and Statistical Inference
Population refers to all items of interest in a statistical
problem (maximum, minimum, mean, mod, median, etc).
May be very large, and sometimes, infinitely large. I

Fakulti Ekonomi Dan Perniagaan
Universiti Malaysia Sarawak
Econometrics (EBQ2074)
Tutorial 2 / Semester 2 (2014/2015)
Submit handwritten answer before the tutorial starts.
Question 1
What is meant by and what is the function of:
(a) Simple regression anal

Fakulti Ekonomi Dan Perniagaan
Universiti Malaysia Sarawak
Econometrics (EBQ2074)
Tutorial 1 / Semester 2 (2014/2015)
Submit handwritten answer before the tutorial starts
Question 1
1.1 What is meant by,
a) Econometrics?
b) Regression analysis?
c) Disturb

LU11
Multicollinearity
Introduction
Practical questions:
1. What is the nature of multicollinearity?
2. Is multicollinearity really a problem?
3. What are the practical consequences?
4. How to detect it?
5. What remedial measure can be taken?
Nature of M

LU4
CLRM Assumption
and BLUE
Learning Objectives
By the end of this lecture you will:
be familiar with the classical assumptions
know the statistical properties of the estimators under
the classical assumptions
The BLUE properties
2
The Classical Assu

LU12
Autoregressive,
Distributed-Lag Models and
Granger Causality Analysis
INTRODUCTION
If the regression model includes not only the current but also
the lagged (past) values of the explanatory variables (the Xs)
it is called distributed-lag model.
e.g:

LU1
The Sciences and
Concept of
Econometrics
Simplicity, simplicity, simplicity: I say let
your affairs be as two or three and
not a hundred or thousand.
Simplify, simplify.
(Hendy David Thoreau: American
Essayist, Poet and Philosopher, 18171862)
Why Econ

LU5
Hypothesis Testing:
Two Variables
Model
1
Learning Objectives
By the end of this lecture you will:
understand the interval estimation
know how to conduct one- and two-sided t-tests of
statistical significance
know how to interpret p-values
2
Inter

LU10
Heteroscedasticity
In this chapter we are interested and seek to answer
the following questions:
1. What is the nature of heteroscedasticity?
2. What are the consequences of the presence of
heteroscedasticity?
3. How to detect heteroscedasticity?
4.

LU6
Multiple
Regression
1
Multiple Regression
Multiple Regression is a simple and natural
extension of bivariate regression.
Permits us to have any number of predictor
variables.
MR means multiple predictors or multiple
influences.
2
The Model
The Mod

Fakulti Ekonomi Dan Perniagaan
Universiti Malaysia Sarawak
Econometrics Tutorial 6 / Semester 2 (2014/2015)
Please submit handwritten answer before tutorial class
start.
Question 1
Consider the following linear regression model:
Y i= 0 + 1 X 1i + 2 X 2 i+

Fakulti Ekonomi Dan Perniagaan
Universiti Malaysia Sarawak
Econometrics Tutorial 1 / Semester 1 (2016/2017)
Please submit handwritten answer before tutorial class
start.
Question 1
(a) Explain the meaning of
i.
Simple regression model
ii.
Multiple regress