Lecturer(s)


Müller Ivo, RNDr. PhDr. Ph.D.

Fišerová Eva, doc. RNDr. Ph.D.

Course content

1 Approach to econometric modeling. 2 A general model; linear regression model. 3 Estimate of regression parameters. 4 Estimate of error variance; statistical verification. 5 Prediction; the test of stability of the model in time. 6 Measures of model fit; the test of regression fit. 7 Multicolinearity; condition index; ridge regression. 8 Dummy variables. Generalized linear model. 9 Tests of homoscedasticity and autocorrelation. 10 Seemingly unrelated regression. 11 Simultaneous equations; structural and reduced forms. 12 Problem of identification; parameter estimation. 13 The final form of simultaneous equations. Dynamic models.

Learning activities and teaching methods

Lecture
 Attendace
 39 hours per semester
 Preparation for the Course Credit
 20 hours per semester
 Preparation for the Exam
 60 hours per semester

Learning outcomes

Mastering statistical procedures appropriate for special data structures in economy.
Comprehension Mastering statistical procedures appropriate for special data structures in economy.

Prerequisites

Motivation to learn

Assessment methods and criteria

Oral exam, Written exam
Credit: active participation in seminars, written test. Exam: oral.

Recommended literature


J. A. Víšek. (1997). Ekonometrie I. Karolinum, Praha.

R. Hušek. (1992). Základy ekonometrie. Skriptum VŠE, Praha.
