This $285,000 federal Project Grant award from the National Science Foundation (NSF) Social, Behavioral, and Economic Sciences (SBE) program (CFDA 47.075) will fund research to develop new statistical methods to guide economic and public policy decisions in rapidly changing environments. The research aims to build on recent advances in statistical decision theory, causal inference, and machine learning to create econometric models that can effectively inform evidence-based policymaking while...
This $301,719 Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program will fund research to develop flexible regression methods that can better measure the economic impacts of climate change. The research will focus on improving statistical techniques to accurately capture the effects of extreme temperature exposure on economic outcomes, which is critical for informing effective climate policy. The work will include...
This Project Grant award of $194,675 from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program supports research to develop new econometric and statistical methods that allow researchers to draw valid conclusions from complex data in a variety of social, behavioral, and medical science settings. The key products and services to be delivered include: Developing a generally applicable reparameterization procedure for informative...
This $226,874 Project Grant awarded by the National Science Foundation (NSF) Social, Behavioral, and Economic Sciences (SBE) program (CFDA 47.075) will fund research to develop new statistical tools for modeling and analyzing dependent data. The project, to be conducted by Cornell University, will focus on advancing methods for handling data dependencies in areas such as local elections, inflation modeling, spatial patterns, and environmental monitoring data. The research aims to provide...
This $289,999 National Science Foundation project grant, awarded under the Mathematical and Physical Sciences program (CFDA 47.049), will fund the development of improved statistical methods, algorithms, and theory for estimation and inference with high-dimensional data at Rutgers, The State University from July 2022 through June 2025. Key products include new statistical methods for regularized estimation, de-biased statistical inference including confidence intervals and regions, and empirical...
This three-year, $674,542 National Science Foundation project grant supports research at the University of California Santa Cruz to develop Bayesian statistical and machine learning methods for analyzing complex survey data from the federal statistical system. The grant falls under the NSF's Social, Behavioral, and Economic Sciences program (CFDA 47.075), which promotes basic research and education in these fields. Specifically, the investigators will extend existing models using data...
This NSF Social, Behavioral, and Economic Sciences (CFDA 47.075) Project Grant award of $572,000 to the National Bureau of Economic Research (NBER) will fund research to develop advanced computational methods for solving heterogeneous-agent macroeconomic models. The research will focus on expanding the capabilities of sequence-space methods to enable the analysis of models with diverse types of heterogeneity, such as location choice and firm-level differences. Key deliverables include new...
The National Science Foundation (NSF) awarded a $249,989 project grant under the Mathematical and Physical Sciences program (CFDA 47.049) to the University of New Hampshire's (UNH) Office of Sponsored Research. The grant supports the development of efficient and robust statistical tools for modeling data with measurement errors, a common challenge in fields like epidemiology and economics. The research aims to improve estimation accuracy and hypothesis testing power across linear, nonlinear, and...
This $375,000 Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program supports the development of statistical methods for analyzing relationships between variables in psychometric studies. The research aims to enable likelihood-based inference for intractable graphical models, providing computationally and statistically efficient tools for full likelihood-based inference, including Bayesian procedures for uncertainty...
This Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program provides $140,482 to fund research aimed at developing a new methodology to distinguish between research findings that can be generalized across populations, places, and time, versus those that are context-specific. The research seeks to address a fundamental challenge in empirical research by creating an improved approach to detect the generalizability of...