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Econ 6905 "Topics in Trade" PhD class at Columbia University

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ECON G6905: Topics in Trade

Jonathan Dingel
Columbia University
Spring 2025

This graduate course covers topics in economic geography and regional economics. We will extensively cover the methods use to estimate, calibrate, simulate, and solve quantitative models in trade, spatial, and urban economics. The methods topics covered will include estimating gravity regressions with high-dimensional fixed effects, computing counterfactual outcomes by exact hat algebra, choosing and implementing numerical solution methods, and conducting Monte Carlo simulations.

This class is part of the trade and spatial second-year PhD sequence. It complements the classes taught by David Weinstein and Donald Davis. I will introduce concepts and models in a self-contained fashion, but I will assume that second-year students are familiar with the material taught in already-completed classes in the sequence.

Logistics

Course materials: github.com/jdingel/econ6905
Class schedule: Wednesdays 8:10 AM - 10:00 AM
Email: jid2106@columbia.edu
Office: IAB 1126B
Office hours: By appointment, please email

Assessment

Grades will be based on assignments (75%) and a final exam (25%).

Assignments will be posted to the GitHub repository. Submit your work via the Courseworks site.

Assignments will come in various forms:

  • Economics: We will ask you to derive a theoretical result or survey an empirical literature.
  • Programming: We will ask you to write a function that solves for equilibrium or estimates a parameter. See comments on computation below.
  • Writing: I will ask to rewrite the abstract of a recent paper.

In addition to course material, the final exam may ask you to propose an original research idea, so you should be thinking about these during our class (and for the rest of your life!).

Computation

Scientific computation is important. I hope that you have already been exposed to the basics. Please glance at Jesus Fernandez-Villaverde's "Computational Methods for Economists" course. My workflow is outlined in my project template.

You have choices to make. See "A Comparison of Programming Languages in Economics." I recommend the Julia language. Julia's advantages are that it is open source and typically faster than Matlab. To get started doing economics in Julia, see Perla, Sargent, and Stachurski's "Lectures in Quantitative Economics." You may submit Julia or Matlab code as homework solutions. Please confer with me before submitting code written in other languages.

Standards for transparency and replicability are rising quickly. The AEA has appointed a Data Editor who will verify that code works prior to accepting papers for publication. Please write code for this class that is transparent and self-contained.

Course Outline and Reading List

I may revise the course outline and reading list during the semester.

Week 1: The CES Armington model

Week 2: Gravity regressions

Week 3: Increasing returns and home-market effects

Week 4: Non-homothetic preferences

Week 5: Agglomeration economies

Week 6: Quantitative spatial models

Week 7: Quantitative urban models

Week 8: Exact hat algebra and calibration

Week 9: The canonical urban model

Week 10: Spatial sorting of skills and sectors

Week 11: Discrete choice estimation and simulations

Week 12: Multi-region firms

Week 13: Spatial environmental economics

Week 14: Research consultations

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Econ 6905 "Topics in Trade" PhD class at Columbia University

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