Computational Macroeconomics - JCM050

• Title: Výpočetní makroekonomie
• Form of teaching: lecture+practicals
• Guaranteed by: CERGE (23-CERGE)
• Faculty: Faculty of Social Sciences
• Actual: from 2026
• Duration in semesters: 1
• Semester: summer
• E-Credits: 9
• Examination process: summer s.:
• Hours per week, examination: summer s.:4/2, Ex [HT]
• Capacity: unlimited / unknown (unknown)
• Maximum number of enrolled students: unlimited
• Min. number of students: unlimited
• 4EU+: no
• Virtual mobility / capacity: no
• State of the course: not taught
• Language: Czech
• Teaching methods: full-time
• Repeated enrollment: 2 / 2 / 1 / 2
• Note: course can be enrolled in outside the study plan; enabled for web enrollment; priority enrollment if the course is part of the study plan
• Guarantor: prof. Ing. Michal Kejak, M.A., CSc.
• Teacher(s): prof. Ing. Michal Kejak, M.A., CSc.; PhDr. Mgr. Ctirad Slavík, Ph.D.
• Pre-requisite : JCM002, JCM017, JCM043

Descriptors

The emphasis of the course is twofold: (i) to extend regression models in the context of cross-section and panel data analysis, (ii) to focus on situations where liner regression models are not appropriate and to study alternative methods. The course prepares you to discuss the estimation of causal parameters and program evaluation and to consider parameter heterogeneity in the second part of the sequence. Examples of applied work will be used throughout the course.

Last update: Kellnerová Eva, Mgr. (08.09.2022)

Literature

The main textbook for the class is Econometric Analysis of Cross Section and Panel Data, J.M. Wooldridge, MIT Press, 2002. Additional references will be provided for the various topics.

 

Last update: Kellnerová Eva, Mgr. (08.09.2022)

Course assessment methods and requirements for successful completion, grading scheme

20% problem sets, 30% midterm, 50% final, both exams are open-book, open-notes

Last update: Kellnerová Eva, Mgr. (08.09.2022)

Syllabus

I Introduction

1 Causal Parameters and Policy Analysis in Econometrics

2 Reminder and Testing Issues

 

II Panel Data Regression Analysis

3 GLS with Panel Data: SURE, RCM, REF

4 E[u|x] is not 0: FEM and Errors in Variables

5 Testing in Panel Data Analysis: Clustering, Minimum Distance

6 GMM and its Application in Panel Data

 

III Qualitative and Limited Dependent Variables

7 Qualitative response models

9.1 Panel Data Applications of Binary Choice Models, Semi-parametric Models

9.2 Multinomial Choice Models

8 Duration Analysis

9 LimDep and Sample Selection

10 Program Evaluation, Matching and Local IV

Last update: Kellnerová Eva, Mgr. (08.09.2022)