Instructors

Gabriel Ahlfeldt (Humboldt University of Berlin)

Dates

31 August - 4 September 2026

Hours

9:30 to 13:00 CEST

Format

In person

Practical Classes

There will be no formal tutorials. However, a teaching assistant will provide an introductory session on MATLAB and the ARSW2015 toolkit.

Intended for

Academic researchers and practitioners interested in urban and spatial economics, including researchers working on policy evaluation, transportation, land use, housing, and regional development.

Prerequisites

Master level course in statistics, econometrics, and microeconomics. Basic familiarity with object-oriented programming concepts and MATLAB would be useful.

Overview

This course provides an introduction to quantitative spatial economics with a focus on theory, quantification, and counterfactual analysis. The goal is to equip participants with a clear understanding of how modern quantitative spatial models are constructed, how their primitives are quantified, and how such models can be used to evaluate counterfactual urban policies.
The course builds around the canonical quantitative urban model with commuting (Ahlfeldt, Redding, Sturm, Wolf, 2015). Lectures emphasize economic intuition and modeling assumptions, while closely following computational toolkits that allow participants to replicate key results and counterfactuals. Rather than requiring independent programming projects, the course adopts a scalable hands-on approach: participants can engage at different depths, ranging from following the theory and results, to actively working through the code and modifying counterfactual scenarios.
By the end of the course, participants will understand how quantitative spatial models translate economic primitives into equilibrium outcomes and how these models can be solved and used for policy-relevant counterfactual analysis within an existing coding infrastructure.
By the end of the course, participants should be able to:

• Understand the core assumptions underlying quantitative spatial models
• Identify the key primitives that need to be quantified in such models
• Understand how equilibrium outcomes are computed in spatial general equilibrium settings
• Interpret and evaluate counterfactual policy experiments in quantitative spatial models
• Work with an existing quantitative spatial model toolkit and understand its code-based implementation

The course consists of lectures that closely follow computational toolkits used to implement the models discussed. Participants are guided through the logic of the models alongside code that reproduces key results and counterfactuals. Selected parts of the code are discussed to illustrate how theoretical objects are translated into computational routines, allowing participants to engage with the material at their preferred level of technical depth.

Topics

• Spatial equilibrium and the quantitative Rosen-Roback model
• Preference heterogeneity and discrete location choice
• The quantitative urban model with commuting (ARSW model)
• Quantification of model primitives using data
• Counterfactual analysis and policy evaluation in quantitative spatial models

Gabriel Ahlfeldt is Professor at Humboldt University in Berlin where he hold the Chair of Econometrics. He is also Visiting Professor at the London School of Economics, faculty of the Berlin School of Economics, and an affiliate of LSE-CEP, CESifo, and CEPR. His primary field is urban economics, but his research cuts across many fields such as environment, finance, labour, political economy, and real estate.  He teaches the PhD course Quantitative Spatial Economics at the Berlin School of Economics and co-organize the Berlin School of Economics Quantitative Spatial Economics Research Seminar (www.bqse.de).