Stephan Heblich

112 posts

Stephan Heblich

Stephan Heblich

@StephanHeblich

Professor, Department of Economics, University of Toronto (@econuoft); affiliated with the Munk School (@munkschool)

Toronto Katılım Eylül 2013
175 Takip Edilen515 Takipçiler
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Trade Diversion (Jonathan Dingel)
Trade Diversion (Jonathan Dingel)@TradeDiversion·
I browsed a preview of Jesse Shapiro's forthcoming Introduction to Quantitative Economics book earlier this year, and it is fantastic. I pre-ordered it just now and will incorporate it into my PhD teaching in the fall. mitpress.mit.edu/9780262051057/…
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JensOehlen
JensOehlen@JensOehlen·
Excited to be on the #EconJobMarket! I study a classic trade-off: exploiting information vs. preserving secrecy. The setting: the Allied breaking of Nazi Enigma codes in WW2. Curious? Thread below. 👇
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Urban Economics Association
Urban Economics Association@UrbanEconomics·
📣 Call for papers 📣 15th European Meeting of the Urban Economics Association May 8 - 9, 2026 CREI, Barcelona Keynotes by Edward Glaeser and Monika Piazzesi. Please submit your paper by January 9. urbaneconomics.org/meetings/emuea…
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Joachim Voth
Joachim Voth@joachim_voth·
🚨 Your spatial regressions might be SPURIOUS! Forthcoming in Stata Journal: A guide to ready-to-use Stata commands to test & correct for strong spatial dependence 🗺️ w/ Sascha Becker + David Boll. Paper: warwick.ac.uk/fac/soc/econom… #EconTwitter #Stata
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Trade Diversion (Jonathan Dingel)
Trade Diversion (Jonathan Dingel)@TradeDiversion·
PhD students underestimate the value of writing well. Faculty skim dozens of JMPs in a short time. If your contribution is unclear, you will get fewer interviews. Editing improves economics papers (RCT): doi.org/10.1016/j.jebo… I am happy to recommend amyedits.net
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Online Spatial & Urban Seminar
📢Correction: Join us next *Monday* at 11:30 ET for an @osus_info seminar! Stijn Van Nieuwerburgh (Colombia) presents “An Alpha in Affordable Housing?” (osus.info) Ben Keys and Jack Liebersohn will be our panelists!
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Jesús Fernández-Villaverde
Jesús Fernández-Villaverde@JesusFerna7026·
I have just posted my survey paper “Deep Learning for Solving Economic Models” on my webpage: sas.upenn.edu/~jesusfv/Deep_… In one or two weeks, it will also circulate as a working paper at the NBER and CEPR. Still, I wanted to let people know already, since I am quite happy with the outcome, largely thanks to some fantastic early feedback I got. As I have often argued, the ongoing revolution in deep learning is transforming how we solve dynamic equilibrium economic models. At its core, solving a model amounts to approximating unknown target functions (such as the value function of agents, a decision rule, or a best response function). Deep learning frequently does a fantastic job at that task. In the paper, I emphasize that this success is not “magic,” but rather the direct consequence of deep learning’s ability to discover better representations of the relevant variables of a model (for example, the state variables). The layers of a neural network transform the input variables into informationally efficient representations that can be more easily approximated. Tom Sargent loves to say that finding the state is an art. Deep learning tries to automatize that art as much as possible. This is why, in many cases, we can now solve high-dimensional problems that were computationally infeasible only a few years ago. Furthermore, the structure of deep networks designed for solving these models, largely linear apart from the non-linearity encapsulated in the activation function, permits massive parallelization. The survey paper is designed to start from the ground up. My intended audience is a first-year graduate student with only a very basic knowledge of solution methods, or even a motivated senior undergraduate. I would very much appreciate feedback. Can you follow the arguments throughout? Are there steps that remain unclear? I have taught courses based on this material at Penn, the Bank of Spain, Cambridge, the ECB, Harvard, Johns Hopkins, Northwestern, Oxford, Princeton, UC Santa Barbara, and Stanford, but I am always looking for fresh eyes to suggest improvements. All the slide decks, with links to the code, are available here: sas.upenn.edu/~jesusfv/teach… under “Machine Learning for Economists.” Eventually, I may use this survey paper and the slide decks as the kernel for something longer, but first, I need to clear my desk of too many ongoing projects.
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Florian Ederer
Florian Ederer@florianederer·
We usually rely on GDP, trade, or wages to study the past. This paper flips the script. It analyzes 630,000 paintings (1400-2000) with machine learning to extract emotions and shows how art tracks living standards, wars, inequality, and even climate shocks.
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Andy Ferrara
Andy Ferrara@Andreas_Ferrara·
My research team (w/ Sam Bazzi, Eric Chyn, Martin Fiszbein, Thomas Pearson, and Pat Testa) is hiring a full-time economics pre-doc for the 2025-26 academic year! If you know someone who might be interested, see the link below: workforcenow.adp.com/mascsr/default…
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John B. Holbein
John B. Holbein@JohnHolbein1·
This preprint (R&R at REStud) argues that slavery accelerated Britain’s industrial revolution.
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