COMPETENCY AND INDONESIAN QUALIFICATION FRAMEWORK BASED SYLLABUS
Course Title
Coordinator
Course Code
Semester
Prerequisite Course
Short Description
Learning Outcome
WEEK
1
2
: Econometrics I
: Prof. Dr. Bambang Juanda
: EKO301/ 3 (2-3)
: Odd/Five
: STK211, EKO 201, EKO 203
: This course is designed to provide the knowledge on the basic principles of econometrics and the skill in estimating the
standard models of econometrics to represent the reality of various issues. The topics taught are, among other,
correlation analysis, regression analysis with ordinary least square (OLS), weighted least square (WLS), indirect
least square (ILS), and two stage least square (2SLS) methods, simple regression, multiple regression, classic
linear regression model assumptions, estimation interval, hypothesis testing, multicollinearity, heteroscedasticity,
autocorrelation, forecasting, and simultaneous equations.
: After completing this course, students are expected to be able to explain and apply the best regression model to
their researches and have the skill in using software (Minitab/SPSS/E-views).
EXPECTED
LEARNIING
OUTCOME
Students understand
econometrics
definition,
econometrics’
modelling
methodology, intervariable relationship
pattern types, and
correlation analysis.
ASSESSMEN
T CRITERIA
Students
can
explain
the
definition
of
econometrics
and
its
modelling
methodology as
well as explain,
calculate, and
interpret
correlation
concept
Students understand Students
can
-
-
-
-
TOPIC
(TEACHING
MATERIAL)
Econometrics
definition
Empirical study
stage by using
econometrics’
modelling
Inter-variable
relationship
pattern types
Correlation
analysis
Cause and result
relationship
criteria
Model definition
TIME
ALLOCA
TION
Lecture, Q & A Lecture:
and/or assignment, 1x (2 x
software use tutorial 50”)
LEARNING
METHOD
LEARNIN
G
SOURCE
BJ : Chap 1
SCORE
WEIGHT
(%)
test 1.5 %
ASSESSMEN
T FORMAT
R: Chap 1
Written
and/or
assignment.
BJ : Chap 2
Written
Tutorial:
1x (3 x
50”)
Lecture, Q & A Lecture:
test 1.5 %
simple
linear
regression
model
concept,
estimate
and
interpret
parameter
coefficient by using
OLS method.
calculate
simple
regression
coefficient
value manually
and
interpret
regression
model
coefficient.
-
3
Students understand
the way and stages
of
hypothesis
testing,
residual
assumption,
and
forecasting and have
the
ability
to
conduct
simple
regression
model
estimation.
Students
can
perform
hypothesis
testing
and
forecasting,
explain
residual/error
assumption,
conduct
estimation
using software
-
-
4
Students understand
multiple
linear
regression
model,
estimate
and
interpret coefficients
and have the ability
Students
can
calculate
multiple
regression
coefficient
values with two
-
-
and
modelling
objective
Simple
linear
regression model
Regression model
vs.
two-way
relationship: the
use of dependent
and independent
variables.
Coefficient
estimation
Coefficient
interpretation
Residual
assumption
Properties of OLS
variables
Hypothesis testing
and
confidence
interval
Dependent
variable analysis
Analysis
of
variance
F-test and T-test
Residual analysis
Analysis
of
variance
Multiple
regression general
model and its
assumption
Regression model
with
two
and/or assignment
1x (2 x
50”)
R: Chap 2
and 3
and/or
assignment.
Tutorial:
1x (3 x
50”)
Lecture, Q & A Lecture:
and/or assignment, 1x (2 x
software use tutorial 50”)
BJ : Chap 2
R: Chap 2
and 3
Written
test 1.5 %
and/or
assignment.
Tutorial:
1x (3 x
50”)
Lecture, Q & A Lecture:
and/or assignment
1x (2 x
50”)
Tutorial:
1x (3 x
BJ : Chap 3
R: Chap 4
Written
test 1.5 %
and/or
assignment.
to conduct multiple
linear
regression
model
estimation
with
two
independent
variables
5
6
Students understand
the way and stages
of
hypothesis
testing,
residual
assumption,
forecasting, analysis
of variance and the
best model selection
as well as have the
ability to conduct
multiple regression
model estimation.
independent
variables
manually and
interpret
regression
model
coefficient as
well as conduct
analysis
of
variance
Students
can
perform
hypothesis
testing
and
forecasting,
explain
residual/error
assumption,
select the best
model
and
conduct
estimation
using software
and can explain
the
output
results
independent
variables
- Complete model
testing: analysis of
variance
- Hypothesis testing
and
confidence
interval
of
regression
coefficient
- Coefficient
of
determination/Go
odness of Fit
- Multiple
regression
in
matrix form
- Partial correlation
and
stepwise
regression
- R2 and AdjustedR2
- Computer output
interpretation
Students understand Students
can - Several functional
regression
model explain
forms
of
with
different regression
regression model
functional forms to model
with - Marginal impact
represent the reality different
and elasticity of
of economic theories functional
various functional
forms
forms
of
50”)
Lecture, Q & A Lecture:
and/or assignment, 1x (2 x
software use tutorial 50”)
BJ : Chap 3
R: Chap 4
Written
test 1.5 %
and/or
assignment.
Tutorial:
1x (3 x
50”)
Lecture, Q & A Lecture:
and/or assignment.
1x (2 x
50”)
Tutorial:
1x (3 x
50”)
BJ : Chap 4
R: Chap 6
Written
test 1.5 %
and/or
assignment.
7
Students
can
develop regression
model
with
qualitative variables
as
independent
variables and have
the
ability
to
conduct regression
model
estimation
using
dummy
variable addition
Students
can
explain
regression
model
with
qualitative
variables
as
independent
variables and
interpret
the
coefficient of
dummy
variable
regression model
- Spurious
nonlinearity
- F-test and t-test
that involve more
than
one
coefficients
Regression
Lecture, Q & A Lecture:
model
with and/or assignment
1x (2 x
independent
50”)
variables in the
form
of
Tutorial:
qualitative
1x (3 x
variable
with
50”)
two categories
(without
interaction with
other
independent
variables
Regression
model
with
independent
variables in the
form
of
qualitative
variable
with
two
category
(with interaction
with
other
independent
variables)
Regression
model
with
qualitative
BJ : Chap 5
R: Chap 7
Written
test 1.5 %
and/or
assignment.
independent
variables with
more than two
categories.
40 %
MID-TEST/UTS
8
9
10
Students understand
the
concept
of
multicollinearity
starting from the
impacts, testing, and
how
to
solve
multicollinearity
issues in regression
model.
Students understand
the
concept
of
heteroscedasticity
starting from the
impacts, testing, and
how
to
solve
heteroscedasticity
issues in regression
model.
Students
can
explain
the
concept
of
multicollinearit
y
and
its
impacts, how to
detect
multicollinearit
y issues and
how to solve
them.
- Classic
linear
regression model
assumption
violation
- Nature
of
multicollinearity
- Multicollinearity
and its impacts
- How to detect
multicollinearity
- How to solve
multicollinearity
issues
- Nature
of
heteroscedasticity
- Heteroscedasticity
and its impacts
- How to detect
heteroscedasticity
- How to solve
heteroscedasticity
issues
Lecture, Q & A Lecture:
and/or assignment
1x (2 x
50”)
Students
can
Lecture, 5 Q & A
explain
the
and/or assignment
concept
of
methods, software
heteroscedastic
use tutorial
ity and its
impacts, how to
detect
heteroscedastic
ity
issues
manually or by
using software
and how to
solve them.
Students understand Students
can - Nature
of Lecture, Q & A
the
concept
of explain
the
autocorrelation
and/or assignment,
BJ : Chap 6
R: Chap 5
Written
test 1.5 %
and/or
assignment.
Tutorial:
1x (3 x
50”)
Lecture:
1x (2 x
50”)
BJ : Chap 7
R: Chap 8
Written
test 1.5 %
and/or
assignment.
Tutorial:
1x (3 x
50”)
Lecture:
1x (2 x
BJ : Chap 8
Written
and/or
test 1.5 %
autocorrelation
starting from the
impacts, testing, and
how
to
solve
autocorrelation
in
regression model.
11
12
Students understand
qualitative
preference models,
estimate
and
interpret coefficient
with
maximum
likelihood method,
hypothesis testing,
residual assumption,
forecasting and the
best model selection.
Students understand
the
issues
and
impacts
of
correlation between
independent
concept
of
autocorrelation
and its impacts,
how to detect
autocorrelation
issues manually
or by using
software and
how to solve
them,
Students
can
explain
the
difference
between
qualitative
preference
models,
interpret
coefficient on
logit
regression,
conduct
hypothesis
testing,
and
select the best
model.
- Autocorrelation
software use tutorial
and its impacts
- How to detect
autocorrelation
- How to solve
autocorrelation
issues
- Introduction
- Binary preference
model
- Linear
opportunity model
- Probit model
- Logit model
- Parameter
coefficient
estimation
- Hypothesis
testing and
confidence
interval
of
regression
coefficient
- Coefficient
interpretation
- Logit
application
Students
can - Correlation
explain
the
between
impacts
of
independent
correlation
variables
and
between
errors
50”)
R: Chap 9
assignment.
BJ : Chap
10
Written
test 1.5 %
and/or
assignment.
Tutorial:
1x (3 x
50”)
Lecture, Q & A Lecture:
and/or assignment
1x (2 x
50”)
R: Chap 12
Tutorial:
1x (3 x
50”)
Lecture, Q & A Lecture:
and/or assignment
1x (2 x
50”)
Tutorial:
BJ : Chap 9
Written
test 1 %
and/or
assignment.
variables and errors
and explain the use
of
estimation
method
13
14
Students understand
simultaneous model
to represent the
dependency between
independent
variables
and
determine the use of
appropriate
estimation model.
Students understand
simultaneous model
estimation methods
independent
- Error
1x (3 x
variables and
measurement on
50”)
errors
and
dependent
and
explain the use
independent
of method that
variables
make
it - Instrumental
possible
variable
estimation method
- Model
specification and
econometric
modelling stages
Students
can - Introduction
Lecture, Q & A Lecture:
explain
and - Identification
and/or assignment
1x (2 x
give illustration
Issues:
50”)
on the use of
a. Order
simultaneous
condition
Tutorial:
equation
as
b. Rank
1x (3 x
well as conduct
condition
50”)
identification to
determine the
use
of
appropriate
estimation
method.
Students
can - Consistent
Lecture, Q & A Lecture:
explain the use
parameter
and/or assignment
1x (2 x
of ILS and
estimation
50”)
2SLS
- ILS method
estimation
- 2SLS method
Tutorial:
methods
- Simultaneous
1x (3 x
according
to
equation
model
50”)
their purposes.
application
FINAL TEST/UAS
BJ : Chap
12
Written
test 1.5 %
and/or
assignment.
R:
Chap 13
BJ : Chap
12
Written
test 1 %
and/or
assignment.
R:
Chap 13
40%
ASSIGNMENT
Literature Reference
1. Juanda, B. 2009. Ekonometrika: Pemodelan dan Pendugaan. Bogor: IPB Press (BJ)
2. Ramanathan, R. 1998. Introductory Econometrics with Applications. 4th edition. The Dryden Press. Fort Worth. (R)
3. Gujarati, D.N. 2004. Basic Econometrics. The McGraw-Hill Companies.
Lecturer and Tutorial Team:
1. Prof. Bambang Djuanda
2. Prof. Noer Azam Achsani
3. Prof. M. Firdaus
4. Dr. Sri Hartoyo
5. Heni Hasanah, M.Si
6. Dian V. Panjaitan, M.Si
ASSESSMENT FORMAT
: Exam and Assignment
Mid-test (UTS)
Final test (UAS)
Tutorial (Assignment, etc.)
: 40 %
: 40 %
: 20 %
20%