Description of Individual Course Units
 Course Unit Code Course Unit Title Type of Course Unit Year of Study Semester Number of ECTS Credits 9102065342002 Econometric Analysis of Cross Sectional Data Elective 1 2 8
Level of Course Unit
Second Cycle
Objectives of the Course
To give practical experience in modelling for cross sectional agricultural data
Name of Lecturer(s)
Prof. Dr. Bulent Miran
Learning Outcomes
 1 Ability of setting up linear econometric model 2 Making statistical tests for econometric models 3 Ability of estimating different mathematical models 4 Ability of applying necesarry tests for estimated models and improving the best models 5 Ability of using estimation results of models on decision making in economics and management
Mode of Delivery
Face to Face
Prerequisites and co-requisities
None
Recommended Optional Programme Components
None
Course Contents
Econometric analysis of cross sectional data from farn management researches by means of least squares through Different mathematical forms, Specificaton tests, Heteroskedasticity tests, Multicollinearity tests, Weigted least squares applications Dummy variables Extensively used econometric software are used for practicing with sample data sets.
Weekly Detailed Course Contents
 Week Theoretical Practice Laboratory 1 Econometric analysis of cross sectional data from farm management researches by means of least squares through Different mathematical forms 2 Econometric analysis of cross sectional data from farn management researches by means of least squares through Different mathematical forms 3 Specificaton tests (Practice with GRETL software) 4 Specificaton tests (Practice with GRETL software) 5 Heteroskedasticity tests (Practice with GRETL software) 6 Heteroskedasticity tests (Practice with GRETL software) 7 Multicollinearity tests (Practice with GRETL software) 8 Midterm exam 9 Multicollinearity tests (Practice with GRETL software) 10 Weigted least squares applications (Practice with GRETL software) 11 Weigted least squares applications (Practice with GRETL software) 12 Dummy variables (Practice with GRETL software) 13 Dummy variables (Practice with GRETL software) 14 Extensively used econometric software are used for practicing with sample data sets (Practice with GRETL software) 15 Extensively used econometric software are used for practicing with sample data sets (Practice with GRETL software) 16 Final exam
Davidson, R., MacKinnon, J.G.,Estimation and inference in econometrics,1993 -Gujarati, D.N.,Basic Econometrics, 3rd Ed.,1995 -Baltagi, B.H.,Econometric Analysis of Panel Data,1995 -Enders, W.,Applied Econometric Time Series,1995 -Intriligator, M.,Bodkin, R.,Hsiao, C.,Econometric Models, Techniques, and Applications, 2nd Ed.,1996 -Hill, C.,Griffits, W.,Judge, G.,Undergraduate Econometrics,1997 -Thomas, R.L.,Modern Econometrics: An Introduction,1997 -Greene, W.,Econometric Analysis, 3rd Ed.,1997 -Ramanathan, R.,Introductory Econometrics with Applications, 4th Ed.,1998 -Greene, W.,Econometric analysis,2000 -Greene, W.,Econometric Analysis, 4th Ed.,2000 -Ruud, P.A.,,An Introduction to Classical Econometric Theory,2000
Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"
Assessment Methods and Criteria
 Term (or Year) Learning Activities Quantity Weight SUM 0 End Of Term (or Year) Learning Activities Quantity Weight SUM 0 SUM 0
Language of Instruction
Turkish
Work Placement(s)
None