EconPapers    
Economics at your fingertips  
 

Forecasting household debt with latent transition modelling

Piotr Bialowolski

Applied Economics Letters, 2017, vol. 24, issue 15, 1088-1092

Abstract: Latent transition modelling (LTM) was used to forecast household debt patterns. A model based on three waves (2011, 2013 and 2015) and over 36,000 responses from the biennial panel study of Polish households – Social Diagnosis – provided data for these forecasts. Based on the fact that transitions between latent states are shaped by previous latent states and socio-economic covariates – age of household head, income and number of household members – we were able to demonstrate LTM as a tool to generate aggregate predictions for both medium- and long-term evolution of the household credit market. The declining tendency for household credit participation rates in Poland is expected in the longer term. In particular, the trend should be supported by decline in the proportion of mortgage debtors. The groups of households indebted for the consumption of durables and those seeking credit outside the banking sector are the groups predicted to remain stable or increase in size.

Date: 2017
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/13504851.2016.1257099 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:apeclt:v:24:y:2017:i:15:p:1088-1092

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEL20

DOI: 10.1080/13504851.2016.1257099

Access Statistics for this article

Applied Economics Letters is currently edited by Anita Phillips

More articles in Applied Economics Letters from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:apeclt:v:24:y:2017:i:15:p:1088-1092