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From Online Job Postings to Economic Insights: A Machine Learning Approach to Structuring Naturally Occurring Data

By Tatjana Dahlhaus, Reinhard Ellwanger, Gabriela Galassi, and Pierre-Yves Yanni

AEA Papers and Proceedings, May 2025

This paper develops a matched vacancy-company dataset by combining daily Canadian online job postings with smartphone-derived visits to points of interest. To address inconsistencies in company names, we enhance natural language processing algorithms for ...

Extracting Statistical Relationships from Observational Data: Predicting with Full or Partial Information

By Guillaume R. Fréchette, Emanuel Vespa, and Sevgi Yuksel

AEA Papers and Proceedings, May 2025

Decision-makers sometimes rely on past data to learn statistical relationships between variables. However, when predicting a target variable, they must adjust how they aggregate past information depending on the observables available. If agents have infor...

Real and Assumed Information

By Ned Augenblick, Matthew Backus, Andrew T. Little, and Don A. Moore

AEA Papers and Proceedings, May 2025

We consider a simplified version of the model in Augenblick et. al (2025), in which people use models to translate data into beliefs and predictions. We posit that they make correct inferences given a model but not unconditionally, forgetting model uncert...

Restructuring the Rate Base

By Steve Cicala

AEA Papers and Proceedings, May 2025

While electricity market restructuring appears to have lowered generation costs, it does not seem to have benefitted consumers. Where did the savings go? This paper evaluates whether the downstream T&D utilities that remained rate regulated capitalize...

Heterogeneous Agent Models

By Ayşe İmrohoroğlu

AEA Papers and Proceedings, May 2025

Heterogeneous agent models have become central to modern macroeconomic research, often replacing the representative agent framework. However, what is core for these frameworks is the use of microfoundations that involve optimizing behavior. The strength o...