Authors: Matias D. Cattaneo, Richard K. Crump, Max H. Farrell, and Yingjie Feng
At the New York Fed, our mission is to make the U.S. economy stronger and the financial system more stable for all segments of society. We do this by executing monetary policy, providing financial services, supervising banks and conducting research and providing expertise on issues that impact the nation and communities we serve.
Our economists engage in scholarly research and policy-oriented analysis on a wide range of important issues.
The mission of the Applied Macroeconomics and Econometrics Center (AMEC) is to provide intellectual leadership in the central banking community in the fields of macro and applied econometrics.
The Center for Microeconomic Data offers wide-ranging data and analysis on the finances and economic expectations of U.S. households.
The monthly Empire State Manufacturing Survey tracks the sentiment of New York State manufacturing executives regarding business conditions.
This ongoing Liberty Street Economics series analyzes disparities in economic and policy outcomes by race, gender, age, region, income, and other factors.
As part of our core mission, we supervise and regulate financial institutions in the Second District. Our primary objective is to maintain a safe and competitive U.S. and global banking system.
The Governance & Culture Reform hub is designed to foster discussion about corporate governance and the reform of culture and behavior in the financial services industry.
Need to file a report with the New York Fed? Here are all of the forms, instructions and other information related to regulatory and statistical reporting in one spot.
The New York Fed works to protect consumers as well as provides information and resources on how to avoid and report specific scams.
The Federal Reserve Bank of New York works to promote sound and well-functioning financial systems and markets through its provision of industry and payment services, advancement of infrastructure reform in key markets and training and educational support to international institutions.
The New York Fed provides a wide range of payment services for financial institutions and the U.S. government.
The New York Fed offers the Central Banking Seminar and several specialized courses for central bankers and financial supervisors.
The New York Fed has been working with tri-party repo market participants to make changes to improve the resiliency of the market to financial stress.
We are connecting emerging solutions with funding in three areas—health, household financial stability, and climate—to improve life for underserved communities. Learn more by reading our strategy.
The Economic Inequality & Equitable Growth hub is a collection of research, analysis and convenings to help better understand economic inequality.
The Governance & Culture Reform hub is designed to foster discussion about corporate governance and the reform of culture and behavior in the financial services industry.
JEL classification: C14, C18, C21
Authors: Matias D. Cattaneo, Richard K. Crump, Max H. Farrell, and Yingjie Feng
Binned scatter plots, or binscatters, have become a popular and convenient tool in applied microeconomics for visualizing bivariate relations and conducting informal specification testing. However, a binscatter on its own is very limited in what it can characterize about the conditional mean. We introduce a suite of formal and visualization tools based on binned scatter plots to restore, and in some dimensions, surpass the visualization benefits of the classical scatter plot. We deliver a comprehensive toolkit for applications, including estimation of conditional mean and quantile functions, visualization of variance and precise quantification of uncertainty, and formal tests of substantive hypotheses such as linearity or monotonicity, and an extension to testing differences across groups. To do so, we give an extensive theoretical analysis of binscatter and related partition-based methods, accommodating nonlinear and potentially non-smooth models, which allows us to treat binary, count, and other discrete outcomes as well. We also correct a methodological mistake related to covariate adjustment present in prior implementations, which yields an incorrect shape and support of the conditional mean. All our results are implemented in publicly available software and showcased with three substantive empirical illustrations. Our empirical results are dramatically different when compared to those obtained using the prevalent methods in the literature.