Understanding Data About the Supplemental Nutrition Assistance Program (SNAP) in the Circana Consumer Network Panel
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DOI: 10.22004/ag.econ.352084
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- Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
- Melvin Stephens, 2008.
"The Consumption Response to Predictable Changes in Discretionary Income: Evidence from the Repayment of Vehicle Loans,"
The Review of Economics and Statistics, MIT Press, vol. 90(2), pages 241-252, May.
- Melvin Stephens Jr., 2003. "The Consumption Response to Predictable Changes in Discretionary Income: Evidence from the Repayment of Vehicle Loans," NBER Working Papers 9976, National Bureau of Economic Research, Inc.
- Bruce D. Meyer & Wallace K. C. Mok & James X. Sullivan, 2015.
"Household Surveys in Crisis,"
Journal of Economic Perspectives, American Economic Association, vol. 29(4), pages 199-226, Fall.
- Bruce D. Meyer & Wallace K.C. Mok & James X. Sullivan, 2015. "Household Surveys in Crisis," NBER Working Papers 21399, National Bureau of Economic Research, Inc.
- Elena Castellari & Chad Cotti & John Gordanier & Orgul Ozturk, 2017. "Does the Timing of Food Stamp Distribution Matter? A Panel‐Data Analysis of Monthly Purchasing Patterns of US Households," Health Economics, John Wiley & Sons, Ltd., vol. 26(11), pages 1380-1393, November.
- Carlson, Andrea C. & Page, Elina Tselepidakis & Zimmerman, Thea Palmer & Tornow, Carina E. & Hermansen, Sigurd, 2019. "Linking USDA Nutrition Databases to IRI Household-Based and Store-Based Scanner Data," Technical Bulletins 291970, United States Department of Agriculture, Economic Research Service.
- Gray, Colin, 2019. "Leaving benefits on the table: Evidence from SNAP," Journal of Public Economics, Elsevier, vol. 179(C).
- Chen Zhen & Mary Muth & Abigail Okrent & Shawn Karns & Derick Brown & Peter Siegel, 2019. "Do differences in reported expenditures between household scanner data and expenditure surveys matter in health policy research?," Health Economics, John Wiley & Sons, Ltd., vol. 28(6), pages 782-800, June.
- Emily Oster, 2018. "Diabetes and Diet: Purchasing Behavior Change in Response to Health Information," American Economic Journal: Applied Economics, American Economic Association, vol. 10(4), pages 308-348, October.
- Hugo Storm & Kathy Baylis & Thomas Heckelei, 2020. "Machine learning in agricultural and applied economics," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 47(3), pages 849-892.
- Tamer, Elie, 2010. "Partial Identification in Econometrics," Scholarly Articles 34728615, Harvard University Department of Economics.
- Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
- Jessica E. Todd, 2015. "Revisiting the Supplemental Nutrition Assistance Program cycle of food intake: Investigating heterogeneity, diet quality, and a large boost in benefit amounts," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 37(3), pages 437-458.
- Bruce D. Meyer & Nikolas Mittag & Robert M. Goerge, 2022. "Errors in Survey Reporting and Imputation and Their Effects on Estimates of Food Stamp Program Participation," Journal of Human Resources, University of Wisconsin Press, vol. 57(5), pages 1605-1644.
- Okrent, Abigail M. & Elitzak, Howard & Park, Timothy & Rehkamp, Sarah, 2018. "Measuring the Value of the U.S. Food System: Revisions to the Food Expenditure Series," Technical Bulletins 277568, United States Department of Agriculture, Economic Research Service.
- Elie Tamer, 2010. "Partial Identification in Econometrics," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 167-195, September.
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