Department of Mathematics and Statistics (Statistics)

Level: PhD level course on econometric methods (first year). Can also be included as a special course to Econometrics module in Statistics (advanced level studies).

General description and aim: This course contains the core material typically taught as a part of a first-year Ph.D. course in econometrics. This course is designed to extend Master’s level statistics (and/or economics/econometrics) studies. The basic methods of modern econometric methods and theory are covered. The intention is that the material will provide a foundation for PhD studies in econometrics.

Content

- Classical finite- and large-sample theory of the linear regression model

- Basics and some extensions of asymptotic theory in econometrics and relationship to the method of maximum likelihood

- Endogeneity, instrumental variables, IV and two-stage least squares estimators

- Heteroskedasticity and generalized least squares

- Generalized method of moments (GMM)

Material: Hayashi, F. (2000). Econometrics. Princeton University Press.

(or equivalent material agreed with the Instructor)

Prerequisites: Students are expected to have the knowledge of linear regression analysis (including the basics of statistical inference) and/or econometrics.That is to the extent covered, for example, in:

Statistics: Tilastollinen päättely I (TILM3561) and II (TILM3562) and Lineaariset ja yleistetyt lineaariset mallit (TILM 3588)

Economics: Ekonometrian johdantokurssi and Syventävä ekonometria I and II.

In addition, studies on time series analysis, such as the basic course on time series analysis (such as TILM 3541), or equivalent, is also recommended.