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Research Spending & Results

Award Detail

Doing Business As Name:University of Wisconsin-Madison
  • Jack R Porter
  • (608) 263-3870
Award Date:07/28/2021
Estimated Total Award Amount: $ 298,699
Funds Obligated to Date: $ 298,699
  • FY 2021=$298,699
Start Date:09/01/2021
End Date:08/31/2024
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.075
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Collaborative Research: Asymptotic Approximations for Sequential Decision Problems in Econometrics
Federal Award ID Number:2117261
DUNS ID:161202122
Parent DUNS ID:041188822
Program Officer:
  • Kwabena Gyimah-Brempong
  • (703) 292-7466

Awardee Location

Street:21 North Park Street
Awardee Cong. District:02

Primary Place of Performance

Organization Name:University of Wisconsin-Madison
Street:1180 Observatory Drive
Cong. District:02

Abstract at Time of Award

Economic and social data are often collected over time. The data collection method may sometimes be adjusted to respond to lessons learnt during the data collection in earlier periods. In these situations, researchers may need to estimate policy effects, test hypotheses, or adjust experimental designs dynamically as new data become available. The estimation method will need to adjust as the data changes. There are currently no efficient methods for drawing inference from data collected in such sequential manner. This project will develop new and innovative methods for analyzing such sequential statistical problems. The project will devise methods that are easy to solve mathematically and allow researchers to properly evaluate dynamically collected data and maximize the efficiency with which the data is used to draw policy conclusions. These methods will be useful in several areas of economics, biostatistics, medicine, and other social sciences. The results of this research will improve methods of policy evaluation, hence improve the functioning of the US economy and governance. This project will develop new methods for analyzing statistical decision problem in dynamic settings. We will extend the limits of experiments framework to incorporate the informational structure in various forms of sequential data collection. The first part of the research will focus on sequential settings where the information available to the analyst is fixed or set exogenously to the collected data. The second part of the project will include settings where sequential collection of data evolves dynamically to reflect information gained from earlier portions of the data. For each of these settings, two key research outputs will be: (i) new information-adapted asymptotic representation theorems; and (ii) a new asymptotic optimality framework and findings. The results of this research will improve methods of policy evaluation, hence improve the functioning of the US economy and governance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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