390035 UK PhD-VGSE: Asymptotics for M-Estimators (2013W)
Prüfungsimmanente Lehrveranstaltung
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SR-Raum 3.307, Oskar-Morgenstern-Platz 1, 1090 Wien, 3.Stock.Monday, 13:15-14:45The level of this course requires knowledge of advanced macroeconomics, advanced microeconomics, and advanced econometrics. ;Students may apply for this course by sending an email to info@vgse.at including their CV, transcript (Sammelzeugnis) and optionally a recommendation of their thesis advisor. ; More information at www.vgse.atCompulsory reading of 'Basic Elements of Asymptitoc Theory' (30 pages) as a preparation for this class. Please contact Verena Konrad (verena.konrad@univie.ac.at) for the file.
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max. 24 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine
Zur Zeit sind keine Termine bekannt.
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
There will be a midterm and a final exam. Both exams carry equal weight.
Mindestanforderungen und Beurteilungsmaßstab
Prüfungsstoff
Literatur
Newey and McFadden (1994), Large Sample Estimation and Hypothesis
Testing, Handbook of Econometrics, Vol 4, Chapter 36.
Pötscher and Prucha (1997) Dynamic Nonlinear Econometric Models: As-
ymptotic Theory, Springer-Verlag.
Pötscher and Prucha (2001), Basic Elements of Asymptotic Theory, in: A
Companion to Theoretical Econometrics (B. Baltagi, ed.), Blackwell Publishers.
Wooldridge (1994), Estimation and Inference for Dependent Processes, Hand-
book of Econometrics, Vol 4, Chapter 45.
Testing, Handbook of Econometrics, Vol 4, Chapter 36.
Pötscher and Prucha (1997) Dynamic Nonlinear Econometric Models: As-
ymptotic Theory, Springer-Verlag.
Pötscher and Prucha (2001), Basic Elements of Asymptotic Theory, in: A
Companion to Theoretical Econometrics (B. Baltagi, ed.), Blackwell Publishers.
Wooldridge (1994), Estimation and Inference for Dependent Processes, Hand-
book of Econometrics, Vol 4, Chapter 45.
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Fr 31.08.2018 08:58
and asymptotic normality proofs for estimators defined through an optimization
problem (e.g., nonlinear least squares, quasi maximum likelihood, GMM, etc.).
You should be familiar with the various convergence concepts for sequences
of random vectors and their interrelations as, e.g., presented in Pötscher and
Prucha (2001).
1. Introduction and Overview: Models, Estimators, Asymptotic Concepts
(Consistency and Asymptotic Normality).
Reading material: Pötscher and Prucha (1997), Chapter 2,
Newey and McFadden (1994), Section 1.
2. Consistency of M-Estimators
Reading material: Pötscher and Prucha (1997), Chapters 3 and 4,
Newey and McFadden (1994), Section 2.
3. Asymptotic Normality of M-Estimators
Reading material: Pötscher and Prucha (1997), Chapters 8 and 9,
Newey and McFadden (1994), Sections 3 and 7.
4. (Uniform) Laws of Large Numbers and Central Limit Theorems
Reading material: Pötscher and Prucha (1997), Chapters 5, 6, and 10,
Wooldridge (1994), Sections 4.2, 4.3.
5. Estimating the Variance-Covariance Matrix of the Asymptotic Distribu-
tion
Reading material: Pötscher and Prucha (1997), Chapter 12,
Newey and McFadden (1994), Section 4,
Wooldridge (1994), Section 4.5.