PhD Econometrics I

ECS508 PhD Econometrics I

Autumn 2020

  • Topics

    This is the first of two courses in econometrics in the PhD program in economics. The goal of the module is to familiarize students with modern econometric techniques and equip them with a deeper understanding of identifying assumptions used by econometric models. The core of the course revolves around identification and estimation theory. Those foundations are later used to introduce more advanced topics with the ultimate goal of equipping future PhDs with a wide range of quantitative tools needed to independently conduct empirical research in different subfields of economics.

    Core topics: we start by discussing the concept of econometric identification and how it can be achieved under different statistical and economic assumptions. We emphasise the difference between parametric and nonparametric identification, structural and reduced form approaches. We then proceed with a rigorous treatment of a commonly used class of extremum estimators, including least squares, maximum likelihood and (generalized) method of moments. Students will be expected to analyse basic properties (consistency, unbiasedness) and derive asymptotic distributions of popular estimators used to recover parameters of linear and nonlinear econometric models. We will also study how different sampling assumptions, especially time dependency in panel and time series settings, affect the set of assumptions necessary for valid statistical inference.

    Additional topics: discrete choice and limited dependent variables, quantile regression, semi- and nonparametric methods (partially linear models, maximum score, kernels, local polynomials, sieves), resampling, measurement error and missing data, machine learning in econometrics (variable selection, causal inference vs. forecasting).

    While major parts of the course are theory-oriented, we also emphasise the importance of practical exercises and proficiency with computer software. These exercises will include replicating results from research papers, critically assessing existing studies and implementing estimation routines on real-world data provided in-class.

  • Learning outcome


    Upon completion of the course, the students will:

    • Understand the meaning of econometric identification
    • Know different types of identifying assumptions and standard identification strategies used in research papers
    • Be able to distinguish between structural and reduced-form approaches, parametric and nonparametric identification
    • Be familiar with different types of extremum estimators for linear and nonlinear econometric models
    • Know methods which can be used to deal with nonlinearities, discrete outcomes, issues with data (eg. outliers, measurement error)


    Upon completion of the course, the students will be able to:

    • Derive a broad class of standard estimators and corresponding standard errors
    • Analyse basic properties of estimators under different assumptions about the data generating process
    • Conduct valid statistical inference in a variety of settings
    • Formulate simple economic and econometric models and propose suitable identification strategies and estimation methods used to recover parameters of interest

    General competencies

    Upon completion of the course, the students will:

    • Be able to use available software to estimate standard models on real world data
    • Be able to evaluate alternative identification strategies
    • Be prepared to independently conduct empirical research

  • Teaching

     Lectures and assignments.

  • Required prerequisites

    Master-level econometrics and statistics or equivalent, including preparation in mathematics (matrix algebra and calculus).

  • Requirements for course approval

    Approved assignments and participation in class.

  • Assessment

    Written 4-hour school exam. The exam must be written in English.

  • Grading Scale


  • Literature

    Lecture notes and selected papers. In addition, chosen chapters from the following textbooks:

    • Hansen, Bruce E. (2019). Econometrics, available online at
    • Hayashi, Fumio (2000). Econometrics, Princeton University Press.
    • Linton, Oliver (2017). Probability, Statistics and Econometrics, Academic Press, 1st edition.


ECTS Credits
Teaching language

Autumn. Offered autumn 2020.

Please note: Due to the present corona situation, please expect parts of this course description to be changed before the autumn semester starts. Particularly, but not exclusively, this relates to teaching methods, mandatory requirements and assessment.

Course responsible

Assistant Professor Mateusz Myśliwski, Department of Economics.