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Welcome to Dynamic Programming and Structural Econometrics

This repository contains teaching materials for lectures in the master level course in Dynamic Programming and Structural Econometrics that I teach at the Economics program at University of Copenhagen.

Previsously recorded lectures are available for a subset lectures and can be found under the Lectures in Dynamic Programming playlist on Bertel Schjerning's YouTube channel. Where available, links to videos below (note that slides may be updated relative to those used in the slides).

Plans for lectures and exercises can be found at this page. Changes might occur.

Repository will be updated as we go a along.

Content

  1. Introduction to Markov Decisions Processes (MDPs) and Dynamic Programming (DP) [Notebook]

  2. Numerical implementation of simple deterministic DP problem: The cake eating problem [Notebook]

  3. Deaton's model and 1d Numerical Integration [Notebook]

  4. Multi-dimensional Integration: Monte Carlo and Quadrature Methods [Notebook] [Video]

  5. Function Approximation [Notebook]

  1. The Nested Fixed Point Algorithm (NFPX) [Slides] [Video 1 - solving Zurcher model] [Video 2 - estimating Zurcher model]

  2. Constrained Optimization Approaches to Structural Estimation (MPEC) [Slides] [Video - MPEC vs NFXP]

  3. Sequential Estimation in Discrete Decision Problems: Nested Pseudo Likelihood (NPL) and CCP estimators [Slides] [Video - NPL and CCP]

  4. Stationary Equilibrium: Equilibrium Trade in Automobile Markets [slides]

Part III: Structural estimation of models with continuous (and discrete) choices

  1. The Buffer-Stock Consumption-Savings Model
  2. Estimating the Buffer-Stock Model
  3. Discrete-Continuous Choice Models
  4. General Equilibrium

Part IV: Solving and estimation of dynamic games**

  1. Solving and estimating static games of incomplete information
  2. Structural Estimation of Dynamic Games using MPEC, NPL and 2-step-PML
  3. Solving and estimating directional dynamic games with multiple equilibria using RLS
  4. Easter: No lecture
  5. Solving and estimating directional dynamic games with multiple equilibria using RLS

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Teaching material for Lectures in Dynamic Programming

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  • Jupyter Notebook 89.4%
  • MATLAB 7.1%
  • Python 3.3%
  • TeX 0.2%