Paul Goldsmith-pinkham
Associate Professor Of Finance
New York, New York, United States
Summary
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Rockstar🎓
Top SchoolPaul Goldsmith-Pinkham is an Associate Professor of Finance at Yale School of Management with over a decade of experience blending academic research and policy work on consumer credit and financial intermediation. Trained as an economist with a PhD from Harvard and a dual BA in Economics and Mathematics from Swarthmore, he has moved between the Federal Reserve Bank of New York and academia, bringing empirical rigor to questions of bankruptcy, credit markets, and causal inference. His research and teaching emphasize applied empirical methods—he maintains a GitHub repo used for a Yale PhD course that implements and visualizes randomization, propensity scores, and treatment effects in R. Known for turning complex econometric concepts into accessible code and visualizations, he bridges theoretical development and practical implementation for students and policymakers. Based in New York, he combines scholarly publication, classroom instruction, and data-driven policy analysis to influence both academic and public debates on consumer finance.
11 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Economics, Doctor of Philosophy (Ph.D.), Economics at Harvard UniversityB.A., Economics,Mathematics, B.A., Economics,Mathematics at Swarthmore College
French
Github Skills (10)
data-visualizations10
data-visualization10
data-visualisation10
causal-inference10
r10
ggplot9
statistical-models9
probability-distribution8
statistics8
distributions8
Programming languages (8)
Github contributions (5)
paulgp/applied-methods-phd
Jan 2021 - Jan 2023
Repo for Yale Applied Empirical Methods PHD Course
Role in this project:
Data Scientist
Data ScientistContributions:1 review, 147 commits, 3 PRs in 2 years
Contributions summary:Paul contributed to the applied empirical methods course by implementing and visualizing concepts related to randomization, propensity scores, and causal inference. They wrote R code to generate histograms, density plots, and survival curves to illustrate theoretical concepts. The user also created code to demonstrate the effect of treatment and model the likelihood function using code examples.
yalepythonr-programminglinear-modelstextbook
paulgp/paulgp.github.io
Jul 2015 - Jan 2023
Website
Contributions:304 commits, 4 PRs, 494 pushes in 7 years 7 months
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