PhD Econometrics I

ECS508 PhD Econometrics I

Spring 2024

Autumn 2024
  • Topics

    This is the first part of the econometrics sequence in the PhD program at NHH. The goal of the module is to familiarize students with modern econometric theory 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 dependence 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 model, kernels, local polynomials, sieves), resampling.

    While major parts of the course are theory-oriented, the problem sets will 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:

    • Be able to explain 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
    • Master the different types of extremum estimators for linear and nonlinear econometric models
    • Be able to apply methods which can be used to deal with endogeneity, nonlinearities, discrete outcomes


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

    • Derive distributions of a broad class of standard estimators
    • 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

    Physical lectures on campus and assignments.

  • Restricted access

    • PhD candidates from NHH
    • PhD candidates from University of Bergen
    • PhD candidates from other higher educational institutions
    • Promising master students if approved by course responsible

  • Required prerequisites

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

  • Compulsory Activity

    Approved assignments and participation in class.

    Compulsory activities (work requirements) are valid for one semester after the semester they were obtained.

  • Assessment

    Individually written 3-hour home exam. The exam must be written in English.

    Re-take is offered the semester after the course was offered for students with valid compulsory activities (work requirements).

  • Grading Scale


  • Computer tools

    Some assignments require use of Stata or R.

  • Literature

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

    • Hansen, Bruce E. (2022). Econometrics, available online at
    • Hayashi, Fumio (2000). Econometrics, Princeton University Press.

  • Permitted Support Material

    Open book exam - all materials including lecture slides, textbooks and notes permitted alongside a bilingual dictionary.


ECTS Credits
Teaching language

Autumn. Offered autumn 2023.

Course responsible

Assistant Professor Mateusz Myśliwski, Department of Economics.