Empirical IO: Dynamic Structural Models

ECS570 Empirical IO: Dynamic Structural Models

Spring 2024

  • Topics

    Day 1
    1. Introduction to structural estimation of dynamic discrete choice models
    2. Nested fixed point (NFXP) estimator
    3. Mathematical programming with equilibrium constraints (MPEC)

    Day 2
    1. Inversion theorem and CCP estimator
    2. Nested pseudo-likelihood (NPL) estimator
    3. Bajari, Benkard, and Levin (BBL) estimator

    Day 3
    1. Dynamic models of equilibrium
    2. Stationary equilibrium model of durable goods market
    3. Non-stationary equilibrium models

    Day 4
    1. Berry–Levinsohn–Pakes (BLP) estimator
    2. Modelling static games
    3. MLE, MPEC, CCP and NPL estimators for static games

    Day 5
    1. Estimation of dynamic games with MPEC, CCP, NPL estimators
    2. Solving dynamic games with multiple equilibria, recursive lexicographical search (RLS)
    3. Nested MLE estimator for dynamic directional games with multiple equilibria (NRLS)

  • Learning outcome

    Knowledge - upon completing the course students will:

      • Apply structural econometric models used in IO to analyze market dynamics
      • Evaluate different estimation technuqies used to estimate games and single-agent dynamic problems
      • Understand the notion of stationary and nonstationary Markov Perfect Equilibrium

    Skill - upon completing the course students will:

    • Be able to develop computer code for the estimation routines in a selected programming language
    • Evaluate the scope of application of the methods within IO

    General competence - upon completing the course students will:

    • Evaluate and choose appropriate econometric methods and apply them to study a variety of questions related to industry dynamics and market competition

  • Teaching

    Plenary lectures and lab sessions

  • Restricted access

    • PhD candidates from Department of Economics, NHH
    • PhD candidates from other Departments at NHH
    • PhD candidates from University of Bergen 
    • PhD candidates from other higher educational institutions

    Promising master students if approved by course responsible

  • Recommended prerequisites

    PhD-level preparation in econometrics, e.g. ECS508 and ECS509 and microeconomics (e.g. ECS504), including static and dynamic game theory.

  • Compulsory Activity

    Attendance in lectures and lab sessions

  • Assessment

    Group project with two to four students.

    The project is due 6 weeks after the last lecture.

  • Grading Scale

    Pass -fail

  • Computer tools



  • Literature

    Lecture notes and a list of papers will be provided by the instructors.


ECTS Credits
Teaching language


Course responsible

Fedor Iskhakov, Professor, Australian National University

Anders Munk-Nielsen, Assistant Professor, University of Copenhagen

John Rust, Professor, Georgetown University

Bertel Schjerning, Professor, University of Copenhagen