Why do households repay their debt in UK during the COVID-19 crisis?
Author
Abstract
Suggested Citation
Download full text from publisher
Other versions of this item:
- Emmanuel Mamatzakis & Mike G. Tsionas & Steven Ongena, 2023. "Why do households repay their debt in UK during the COVID-19 crisis?," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 50(8), pages 1789-1823, April.
References listed on IDEAS
- Georgarakos, Dimitris & Kenny, Geoff, 2022.
"Household spending and fiscal support during the COVID-19 pandemic: Insights from a new consumer survey,"
Journal of Monetary Economics, Elsevier, vol. 129(S), pages 1-14.
- Georgarakos, Dimitris & Kenny, Geoff, 2022. "Household spending and fiscal support during the COVID-19 pandemic: insights from a new consumer survey," Working Paper Series 2643, European Central Bank.
- Thomas Hale & Noam Angrist & Rafael Goldszmidt & Beatriz Kira & Anna Petherick & Toby Phillips & Samuel Webster & Emily Cameron-Blake & Laura Hallas & Saptarshi Majumdar & Helen Tatlow, 2021. "A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker)," Nature Human Behaviour, Nature, vol. 5(4), pages 529-538, April.
- Dong Hwan Oh & Andrew J. Patton, 2021. "Better the Devil You Know: Improved Forecasts from Imperfect Models," Finance and Economics Discussion Series 2021-071, Board of Governors of the Federal Reserve System (U.S.).
- Hansen, Stephen & Carvalho, Vasco & GarcÃa, Juan Ramón & Ortiz, Alvaro & Rodrigo, Tomasa & RodrÃguez Mora, José V & Ruiz, Pep, 2020.
"Tracking the COVID-19 Crisis with High-Resolution Transaction Data,"
CEPR Discussion Papers
14642, C.E.P.R. Discussion Papers.
- Carvalho, V & Garcia, Juan R. & Hansen, S. & Ortiz, A. & Rodrigo, T. & More, J. V. R., 2020. "Tracking the COVID-19 Crisis with High-Resolution Transaction Data," Cambridge Working Papers in Economics 2030, Faculty of Economics, University of Cambridge.
- Christopher Roth & Johannes Wohlfart, 2020.
"How Do Expectations about the Macroeconomy Affect Personal Expectations and Behavior?,"
The Review of Economics and Statistics, MIT Press, vol. 102(4), pages 731-748, October.
- Roth, Christopher & Wohlfart, Johannes, 2018. "How do expectations about the macroeconomy affect personal expectations and behavior?," IMFS Working Paper Series 128, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
- Christopher Roth & Johannes Wohlfart, 2018. "How Do Expectations About the Macroeconomy Affect Personal Expectations and Behavior?," CESifo Working Paper Series 7154, CESifo.
- Koutsomanoli-Filippaki, Anastasia I. & Mamatzakis, Emmanuel C., 2011.
"Efficiency under quantile regression: What is the relationship with risk in the EU banking industry?,"
Review of Financial Economics, Elsevier, vol. 20(2), pages 84-95, May.
- Anastasia I. Koutsomanoli‐Filippaki & Emmanuel C. Mamatzakis, 2011. "Efficiency under quantile regression: What is the relationship with risk in the EU banking industry?," Review of Financial Economics, John Wiley & Sons, vol. 20(2), pages 84-95, May.
- Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006.
"Predicting volatility: getting the most out of return data sampled at different frequencies,"
Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
- Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," NBER Working Papers 10914, National Bureau of Economic Research, Inc.
- Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," CIRANO Working Papers 2004s-19, CIRANO.
- Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004.
"The MIDAS Touch: Mixed Data Sampling Regression Models,"
University of California at Los Angeles, Anderson Graduate School of Management
qt9mf223rs, Anderson Graduate School of Management, UCLA.
- Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," CIRANO Working Papers 2004s-20, CIRANO.
- Anna Zabai, 2020. "How are household finances holding up against the Covid-19 shock?," BIS Bulletins 22, Bank for International Settlements.
- Tarne, Ruben & Bezemer, Dirk & Theobald, Thomas, 2022. "The effect of borrower-specific loan-to-value policies on household debt, wealth inequality and consumption volatility: An agent-based analysis," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
- Kubota, So & Onishi, Koichiro & Toyama, Yuta, 2021. "Consumption responses to COVID-19 payments: Evidence from a natural experiment and bank account data," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 1-17.
- Ghysels, Eric & Wright, Jonathan H., 2009.
"Forecasting Professional Forecasters,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 504-516.
- Eric Ghysels & Jonathan H. Wright, 2006. "Forecasting professional forecasters," Finance and Economics Discussion Series 2006-10, Board of Governors of the Federal Reserve System (U.S.).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- MAMATZAKIS, emmanuel & MAMATZAKIS, E, 2022.
"Understanding the impact of travel on wellbeing: evidence for Great Britain during the pandemic,"
MPRA Paper
121782, University Library of Munich, Germany.
- MAMATZAKIS, emmanuel & MAMATZAKIS, E, 2022. "Understanding the impact of travel on wellbeing: evidence for Great Britain during the pandemic," MPRA Paper 112974, University Library of Munich, Germany.
- Emmanuel C. Mamatzakis & Steven Ongena & Mike G. Tsionas, 2023. "The response of household debt to COVID-19 using a neural networks VAR in OECD," Empirical Economics, Springer, vol. 65(1), pages 65-91, July.
- Claudia Foroni & Massimiliano Marcellino, 2013.
"A survey of econometric methods for mixed-frequency data,"
Economics Working Papers
ECO2013/02, European University Institute.
- Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
- Lixiong Yang & Mingjian Ren & Jianming Bai, 2025. "Threshold mixed data sampling logit model with an application to forecasting US bank failures," Empirical Economics, Springer, vol. 68(1), pages 433-477, January.
- Michael P. Clements & Ana Beatriz Galvão, 2007. "Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US Output Growth," Working Papers 616, Queen Mary University of London, School of Economics and Finance.
- Warmedinger, Thomas & Paredes, Joan & Asimakopoulos, Stylianos, 2013. "Forecasting fiscal time series using mixed frequency data," Working Paper Series 1550, European Central Bank.
- Stylianos Asimakopoulos & Joan Paredes & Thomas Warmedinger, 2020. "Real‐Time Fiscal Forecasting Using Mixed‐Frequency Data," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(1), pages 369-390, January.
- Zhang, Yue-Jun & Wang, Jin-Li, 2019. "Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models," Energy Economics, Elsevier, vol. 78(C), pages 192-201.
- Rong Fu & Luze Xie & Tao Liu & Juan Huang & Binbin Zheng, 2022. "Chinese Economic Growth Projections Based on Mixed Data of Carbon Emissions under the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
- Clements, Michael P. & Galvao, Ana Beatriz, "undated".
"Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US output growth and inflation,"
Economic Research Papers
269743, University of Warwick - Department of Economics.
- Clements, Michael P & Galvão, Ana Beatriz, 2006. "Macroeconomic Forecasting with Mixed Frequency Data : Forecasting US output growth and inflation," The Warwick Economics Research Paper Series (TWERPS) 773, University of Warwick, Department of Economics.
- Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.
- Huiwen Lai & Eric C. Y. Ng, 2020. "On business cycle forecasting," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-26, December.
- Ambrocio, Gene & Hasan, Iftekhar, 2022. "Belief polarization and Covid-19," Bank of Finland Research Discussion Papers 10/2022, Bank of Finland.
- Özer Karagedikli & Murat Özbilgin, 2019. "Mixed in New Zealand: Nowcasting Labour Markets with MIDAS," Reserve Bank of New Zealand Analytical Notes series AN2019/04, Reserve Bank of New Zealand.
- J. Isaac Miller, 2014.
"Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures,"
Journal of Financial Econometrics, Oxford University Press, vol. 12(3), pages 584-614.
- J. Isaac Miller, 2012. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Working Papers 1211, Department of Economics, University of Missouri.
- Michelle T. Armesto & Kristie M. Engemann & Michael T. Owyang, 2010. "Forecasting with mixed frequencies," Review, Federal Reserve Bank of St. Louis, vol. 92(Nov), pages 521-536.
- Qian Chen & Xiang Gao & Shan Xie & Li Sun & Shuairu Tian & Shigeyuki Hamori, 2021. "On the Predictability of China Macro Indicator with Carbon Emissions Trading," Energies, MDPI, vol. 14(5), pages 1-24, February.
- Qiu, Yue, 2020. "Forecasting the Consumer Confidence Index with tree-based MIDAS regressions," Economic Modelling, Elsevier, vol. 91(C), pages 247-256.
- Selma Toker & Nimet Özbay & Kristofer Månsson, 2022. "Mixed data sampling regression: Parameter selection of smoothed least squares estimator," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 718-751, July.
- Lixiong Yang, 2022. "Threshold mixed data sampling (TMIDAS) regression models with an application to GDP forecast errors," Empirical Economics, Springer, vol. 62(2), pages 533-551, February.
More about this item
Keywords
; ; ; ; ;JEL classification:
- G0 - Financial Economics - - General
- G00 - Financial Economics - - General - - - General
- G1 - Financial Economics - - General Financial Markets
- I1 - Health, Education, and Welfare - - Health
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:118785. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.
Printed from https://ideas.repec.org/p/pra/mprapa/118785.html