Life after (Soft) Default
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- De Giorgi, Giacomo & Naguib, Costanza, 2024. "Life after (soft) default," European Economic Review, Elsevier, vol. 167(C).
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- Robert Collinson & John Eric Humphries & Nicholas Mader & Davin Reed & Daniel Tannenbaum & Winnie van Dijk, 2024.
"Eviction and Poverty in American Cities,"
The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(1), pages 57-120.
- Robert Collinson & John Eric Humphries & Nicholas S. Mader & Davin K. Reed & Daniel I. Tannenbaum & Winnie van Dijk, 2022. "Eviction and Poverty in American Cities," NBER Working Papers 30382, National Bureau of Economic Research, Inc.
- Robert Collinson & John Eric Humphries & Nicholas Mader & Davin Reed & Daniel Tannenbaum & Winnie van Dijk, 2023. "Eviction and Poverty in American Cities," Working Papers 23-37, Center for Economic Studies, U.S. Census Bureau.
- Winnie van Dijk & Robert Collinson & John Eric Humphries & Nicholas Mader & Davin Reed & Daniel Tannenbaum, 2022. "Eviction and Poverty in American Cities," Working Papers 2022-24, Human Capital and Economic Opportunity Working Group.
- Robert Collinson & John Eric Humphries & Nicholas Mader & Davin Reed & Daniel Tannenbaum & Winnie Van Dijk, 2022. "Eviction and Poverty in American Cities," Cowles Foundation Discussion Papers 2344, Cowles Foundation for Research in Economics, Yale University.
- Robert Collinson & John Eric Humphries & Nicholas Mader & Davin Reed & Daniel Tannenbaum & Winnie van Dijk, 2022. "Eviction and Poverty in American Cities," Working Papers 21-40, Federal Reserve Bank of Philadelphia.
- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018.
"Double/debiased machine learning for treatment and structural parameters,"
Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey & James Robins, 2017. "Double/debiased machine learning for treatment and structural parameters," CeMMAP working papers CWP28/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey & James Robins, 2017. "Double/debiased machine learning for treatment and structural parameters," CeMMAP working papers 28/17, Institute for Fiscal Studies.
- Diamond, Rebecca & Guren, Adam & Tan, Rose, 2020.
"The Effect of Foreclosures on Homeowners, Tenants, and Landlords,"
Research Papers
3877, Stanford University, Graduate School of Business.
- Rebecca Diamond & Adam Guren & Rose Tan, 2020. "The Effect of Foreclosures on Homeowners, Tenants, and Landlords," NBER Working Papers 27358, National Bureau of Economic Research, Inc.
- Janet Currie & Erdal Tekin, 2015.
"Is There a Link between Foreclosure and Health?,"
American Economic Journal: Economic Policy, American Economic Association, vol. 7(1), pages 63-94, February.
- Janet Currie & Erdal Tekin, 2011. "Is there a Link Between Foreclosure and Health?," NBER Working Papers 17310, National Bureau of Economic Research, Inc.
- Stefania Albanesi & Domonkos F. Vamossy, 2019.
"Predicting Consumer Default: A Deep Learning Approach,"
NBER Working Papers
26165, National Bureau of Economic Research, Inc.
- Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," Working Papers 2019-056, Human Capital and Economic Opportunity Working Group.
- Albanesi, Stefania & Vamossy, Domonkos, 2019. "Predicting Consumer Default: A Deep Learning Approach," CEPR Discussion Papers 13914, C.E.P.R. Discussion Papers.
- Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," Papers 1908.11498, arXiv.org, revised Oct 2019.
- Peter Ganong & Pascal J. Noel, 2020. "Why Do Borrowers Default on Mortgages?," NBER Working Papers 27585, National Bureau of Economic Research, Inc.
- Albanesi, Stefania & Nosal, Jaromir, 2015.
"Insolvency After the 2005 Bankruptcy Reform,"
CEPR Discussion Papers
10533, C.E.P.R. Discussion Papers.
- Jaromir Nosal & Stefania Albanesi, 2016. "Insolvency after the 2005 Bankruptcy Reform," 2016 Meeting Papers 1147, Society for Economic Dynamics.
- Stefania Albanesi & Jaromir B. Nosal, 2015. "Insolvency after the 2005 bankruptcy reform," Staff Reports 725, Federal Reserve Bank of New York.
- Albanesi, Stefania & Nosal, Jaromir, 2015. "Insolvency After the 2005 Bankruptcy Reform," Economics Series 312, Institute for Advanced Studies.
- Stefania Albanesi & Jaromir Nosal, 2018. "Insolvency After the 2005 Bankruptcy Reform," NBER Working Papers 24934, National Bureau of Economic Research, Inc.
- Luigi Guiso & Paola Sapienza & Luigi Zingales, 2013.
"The Determinants of Attitudes toward Strategic Default on Mortgages,"
Journal of Finance, American Finance Association, vol. 68(4), pages 1473-1515, August.
- Luigi Guiso & Paola Sapienza & Luigi Zingales, 2010. "The Determinants of Attitudes towards Strategic Default on Mortgages," Economics Working Papers ECO2010/31, European University Institute.
- Athey, Susan & Imbens, Guido W., 2019.
"Machine Learning Methods Economists Should Know About,"
Research Papers
3776, Stanford University, Graduate School of Business.
- Susan Athey & Guido Imbens, 2019. "Machine Learning Methods Economists Should Know About," Papers 1903.10075, arXiv.org.
- Callaway, Brantly & Sant’Anna, Pedro H.C., 2021.
"Difference-in-Differences with multiple time periods,"
Journal of Econometrics, Elsevier, vol. 225(2), pages 200-230.
- Brantly Callaway & Pedro H. C. Sant'Anna, 2018. "Difference-in-Differences with Multiple Time Periods," Papers 1803.09015, arXiv.org, revised Dec 2020.
- Card, David, 2001.
"Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems,"
Econometrica, Econometric Society, vol. 69(5), pages 1127-1160, September.
- David Card, 2000. "Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems," NBER Working Papers 7769, National Bureau of Economic Research, Inc.
- Christine A. Parlour & Uday Rajan, 2001. "Competition in Loan Contracts," American Economic Review, American Economic Association, vol. 91(5), pages 1311-1328, December.
- Helena Bach & Pietro Campa & Giacomo De Giorgi & Jaromir Nosal & Davide Pietrobon, 2023.
"Born to Be (Sub)Prime: An Exploratory Analysis,"
AEA Papers and Proceedings, American Economic Association, vol. 113, pages 166-171, May.
- Bach, Helena & Campa, Pietro & De Giorgi, Giacomo & Nosal, Jaromir & Pietrobon, Davide, 2023. "Born to be (sub)Prime: An Exploratory Analysis," CEPR Discussion Papers 17847, C.E.P.R. Discussion Papers.
- Gelman, Michael & Kariv, Shachar & Shapiro, Matthew D. & Silverman, Dan & Tadelis, Steven, 2020.
"How individuals respond to a liquidity shock: Evidence from the 2013 government shutdown,"
Journal of Public Economics, Elsevier, vol. 189(C).
- Michael Gelman & Shachar Kariv & Matthew D. Shapiro & Dan Silverman & Steven Tadelis, 2015. "How Individuals Respond to a Liquidity Shock: Evidence from the 2013 Government Shutdown," NBER Working Papers 21025, National Bureau of Economic Research, Inc.
- David B. Gross & Nicholas S. Souleles, 2002. "Do Liquidity Constraints and Interest Rates Matter for Consumer Behavior? Evidence from Credit Card Data," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(1), pages 149-185.
- Marieke Bos & Emily Breza & Andres Liberman, 2018.
"The Labor Market Effects of Credit Market Information,"
The Review of Financial Studies, Society for Financial Studies, vol. 31(6), pages 2005-2037.
- Marieke Bos & Emily Breza & Andres Liberman, 2016. "The Labor Market Effects of Credit Market Information," NBER Working Papers 22436, National Bureau of Economic Research, Inc.
- S. Michael Giliberto & Arthur L. Houston, 1989. "Relocation Opportunities and Mortgage Default," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 17(1), pages 55-69, March.
- 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.
- Mnasri, Ayman, 2018. "Downpayment, mobility and default: A welfare analysis," Journal of Macroeconomics, Elsevier, vol. 55(C), pages 235-252.
- Giacomo De Giorgi & Matthew Harding & Gabriel Vasconcelos, 2021. "Predicting Mortality from Credit Reports," Papers 2111.03662, arXiv.org.
- Donghoon Lee & Wilbert Van der Klaauw, 2010. "An introduction to the FRBNY Consumer Credit Panel," Staff Reports 479, Federal Reserve Bank of New York.
- Peter Ganong & Pascal J. Noel, 2020. "Why Do Borrowers Default on Mortgages? A New Method For Causal Attribution," Working Papers 2020-100, Becker Friedman Institute for Research In Economics.
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More about this item
JEL classification:
- J61 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Geographic Labor Mobility; Immigrant Workers
- G51 - Financial Economics - - Household Finance - - - Household Savings, Borrowing, Debt, and Wealth
- D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BAN-2023-07-17 (Banking)
- NEP-RMG-2023-07-17 (Risk Management)
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