ASSIST. PROF. DR. YRD. DOÇ. DR. Havvanur Feyza ERDEM,

Language of instruction

Turkish

Professional practise ( internship )

None

The aim of the course:

This course is complementary of Introduction to Econometrics and Econometric Theory courses. Main objective of this course is to make econometric analyses by using Eviews.

Learning Outcomes

CTPO

TOA

Upon successful completion of the course, the students will be able to :

LO - 1 :

learn what econometrics tools are

1,2,3,4,5,6,7

1,4

LO - 2 :

learn how to use econometric tools

1,2,3,4,5,6,7

1,4

LO - 3 :

learn how to apply econometric tools to economic problems

1,2,3,4,5,6,7

1,4

LO - 4 :

learn how to analyze economic problems by utilizing from econometric tools

1,2,3,4,5,6,7

1,4

LO - 5 :

learn how to solve economic problems by utilizing from econometric tools

1,2,3,4,5,6,7

1,4

CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome

Contents of the Course

Fundamentals of Eviews. Estimation of two variable regression model: the method of ordinary least squares (OLS) and the method of maximum likelihood (ML). Interval estimation and hypothesis testing: confidence intervals for regression coefficients and error variance, hypothesis testing via confidence interval and the test of significance approaches, Regression analysis and analysis of variance. Regression through the origin, scaling and units of measurement. Functional form of regression models: log-log, log-lin, lin-log and reciprocal models. Hypothesis testing in multiple regression: hypothesis testing about individual partial regression coefficients, testing the overall significance of the sample regression, testing the equality of two regression coefficients, testing linear equality restrictions (the t test approach and the f test approach), testing for structural stability of regression models, testing the functional form of regression. Prediction with multiple regression. Multicollinearity: estimation in the presence of perfect multicollinearity, estimation in the presence of high but imperfect multicollinearity, consequences of multicollinearity, detection of multicollinearity, remedial measures. Heteroscedasticity: OLS estimation in the presence of heteroscedasticity, the method of generalized least squares, consequences of using OLS in the presence of heteroscedasticity, detection of heteroscedasticity, remedial measures.

Course Syllabus

Week

Subject

Related Notes / Files

Week 1

Basics of E-views and RATS

Week 2

Estimation of simple resgression, Ordinary Least Squares (OLS)

Week 3

Determination coefficient, t-test, F-test

Week 4

Interval estimation and Hypothesis testing

Week 5

Regression analysis and variance analysis

Week 6

Functional form of regression equation, MWD test, RESET test

Week 7

Functional form of regression equation, log-log, lin-log, log-lin and reciprocal models

Week 8

t and F test in multivariate regressions

Week 9

Mid-term exam

Week 10

Structural stability test

Week 11

An Application

Week 12

Multicolinearity problem

Week 13

Detection and eliminatibg multicolinearity problem

Week 14

Heteroscedasticity problem, detection of heteroscedasticity

Week 15

Eliminating the heteroscedascity problem

Week 16

End-of-term exam

Textbook / Material

1

Yamak, R. ve Köseoğlu, M. 2006, Uygulamalı İstatistik ve Ekonometri, Aksakal Yayınları, Trabzon.