Advanced Econometrics 1
Level:
Doctoral Course Status:
Mandatory Course Status:
Elective CEU code:
ECON 6105 - Advanced Econometrics I CEU credits:
4 ECTS credits:
8 Term:
2nd Academic year:
2009/2010 Academic year:
2010/2011 Academic year:
2011/2012 Semester:
Winter Host Unit:
Department of Economics Academic Program:
CEU Instructor(s):
Laszlo Matyas Additional information:
CORE: for PhD;
ELECTIVE: for Economics MA 2nd year;
Assumed Background
• Classical linear regression model, assumptions
• OLS and GLS estimators
• OLS finite sample properties
• GLS finite sample properties
• Model with AR(1) disturbances and heteroscedasticity
• Basic hypothesis testing (null and alternative, significance level,
critical value)
• Basic tests in the Classical linear regression model (t, F, R^2,
heteroscedasticity and autocorrelation, etc.)
• Maximum likelihood estimation
• Elements of time series analysis: unit root and cointegration
•
Preliminary Syllabus
• Parametric, non-parametric methods
• Identification
• Asymptotic Theory:
• OLS, GLS, and FGLS estimators' asymptotic properties
• Likelihood theory
• Quasi (Pseudo) maximum likelihood estimation
• Extremum estimation
• Empirical likelihood
• Hypothesis testing in econometrics: revisited
• Instrumental variables estimation (IV)
• Generalised Method of Moments estimation (GMM)
• Testing for overidentifying restrictions
• Binary Choice Models and Multinomial Models
• Truncated and Censored models
• Duration and Count data models
• Proportional Data and Sample Selectivity
• Restricted estimation, Mixed estimation
• Pre-test estimators
• Linear models for panel data
• Selectivity and rotating panels
• Non-linear models for panel data Learning Outcomes:
i) development of analytical skills to
a level where students can evaluate critically any econometrics research output
and design and carry out themselves such research projects. ii) development of
technical skills which enable students to understand and apply highly
sophisticated econometric tools. iii) Development of computer programming skills
in order to be able to implement i) and ii). iv) Development of individual time
management of learning, through frequent and individualized assessment. Assessment :
There will be 5-6 individualized assignments during the term
with a total of 50% weight in the assessment, and a final examination with also
50% weight. In order to get the minimum pass grade (C+) for the course, at
least 50% of the marks must be obtained in both components of the assessment
(assignments and final exam). Full description:
The main aim of this course is to provide the
students with an advanced training in econometric theory. Students successfully
completing this course should be able to apply most of the modern econometric
tools in empirical studies and, also, to understand and evaluate new research
results in many important areas of econometrics.
