SYLLABUS
FACULTY ECONOMIC AND BUSINESS
INTERNATIONAL PROGRAM FOR ISLAMIC ECONOMICS AND FINANCE (IPIEF)
1 Name of Course / Module Econometrics I
2 Course Code IE7425
3 Credit Value 3
4 Objective
At the end of this course, students are expected to be capable of applying various econometric models to deal with the problems related to both conventional and Islamic Economics.
5 Learning Outcomes
There are four areas in assessing the outcomes of studying this subject:
1. Knowledge and understanding
a. Students are able to possess a keen understanding of statistics
b. Able to fully understand the basic concept of econometrics
c. Able to have sound knowledge of econometric models
2. Intellectual Skill
a. Students are expected to gain a deep knowledge about the deviation of classic assumptions
b. To analyze in detail the multiple regression model 3. Practical Skill
a. Students gain real experience of analyzing the econometrics issue by utilizing SPSS and Eviews software
b. Have the experience of conducting research on econometrics
4. Managerial Skill and Attitude
a. Students are able to improve their capability for doing research
b. Able to get the experience of constructing the econometric model in studying economic activity.
6 Synopsis This course is of economic tools to tackle the economic and business problems quantitatively by using
econometrics approach. Several basic concepts of econometric model will extensively be explained and practiced throughout the course hence the students are able to demonstrate profound understanding of such an approach adopted to solve the very foundation of economic and finance issues.
The selected topics to be discussed include: basic concept of econometrics, regression analysis, interval estimation and hypothesis test, the problems of estimation, inference, regression model of dummy variable, classic assumption, model construction and diagnosis test.
7 References
Required Materials/Resources:
Textbook:
1. Gujarati, Damodar. (1978). Basic Econometrics.
McGraw-Hill, Inc.
2. Basuki, Agus Tri and Prawoto, Nano. (2016). Analisis Regresi dalam Penelitian Ekonomi dan Bisnis.
Jakarta: Raja Grafindo.
8 Lecturer Agus Tri Basuki, SE., M.Si
9 Correspondence
Lecturer Room, E4 Building 2nd Floor E-mail: [email protected] Phone: +62 816676907 (WA) 10 Consultation time By appointment
11 Academic Evaluation
No Assessment Aspects Percentage
1
Knowledge 1
2 3
Competency test (CT) 1
CT 2 CT3
10
20 20
2
Intellectual and Practical skill
1 2 3
Paper Presentation Class participation
20 10 10
3 Managerial Cooperation 10
Total 100
COURSE OUTLINE
Meeting Course Material Material Readings
1 Course introduction and overview a. The definition of econometrics b. Methodology of econometrics c. Types of econometrics
d. Using the tools
2 Basic concept of regression analysis a. Basic concept of regression
b. The relation between statistic and deterministic c. Regression, causality and correlation
d. Terminology and notation
Ch. 1
3 Regression analysis: Two variables a. The example of hypothesis
b. The concept of population regression
c. The definition of linier, stochastic, and disturbance terms
d. The function of sample regression
Ch. 2
4 Regression model for two variables: Estimation problem a. Ordinary least square method
b. Model of linier classic regression
c. Standard error estimation of least square d. Coefficient of determination
Ch. 3
5 Competency Test 1 6 Linier regression model
a. Probability distribution of interference factor b. Maximum likelihood method
Ch. 4
7 Regression for two variables: Interval estimation and hypothesis test
a. The concept of interval estimation b. Confidence interval
c. Hypothesis test
Ch. 5
8 Problem estimation for multiple regression analysis a. Regression model of three variables
b. The explanation of the coefficient sign
c. Estimating ordinary least square and maximum likelihood
d. Coefficient determination
Ch. 7
9 Competency Test II
10 Inference problems for multiple regression analysis a. The assumption of normality
b. Testing the hypothesis
c. Examination of the significance level d. Assessment of restrictions in linier equation e. Calibration of parameter stability
11 Regression model for dummy variable
a. The concept of dummy variable regression b. ANOVA model for two qualitative variables c. Regression model with the mixed regressor both
quantitative and qualitative
d. Some applications of dummy variables
Ch. 8
12 Application in Economics and Business Ch. 9 13 Competency Test III
14 Multicollinearity a. The definition
b. Estimation of multicollinearity
c. Practical consequences of multicollinearity d. Detection and solution
Ch. 10
15 Heteroscedasticity a. The definition
b. Estimation of Heteroscedasticity
c. Practical consequences of Heteroscedasticity d. Detection and solution
Ch. 11
16 Autocorrelation a. The definition
b. Estimation of Autocorrelation
c. Practical consequences of Autocorrelation d. Detection and solution
Ch. 12
17 Econometrics Modelling
a. The criteria to choose the model b. Types of error specification and its test c. The error of measurement
d. Nested and Non-nested Model
Ch. 13
18 Competency Test IV
Evaluation:
The key indicators of this course are:
1. The percentage of students who earn A and B is much higher than 25 per cent 2. The proportion of the students who gain D is less than 10 per cent
3. The average for student attendance is higher than 75 per cent
4. Grading System
Grading and Weight Interval (%)
A A≥80
AB 75≤AB<80
B 65≤B<75
BC 60≤BC<65
C 50≤C<60
D 35≤D<50
E <35
