Forecasting household debt with latent transition modelling

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.

KEYWORDS:

• Household debt
• forecasting
• latent transition model
• latent/hidden Markov models

JEL CLASSIFICATION:

• C33
• C38
• C53
• D14

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

¹ Measurement levels for debt value (in terms of monthly income) were: zero, <1, 1–3, 3–6, 6–12, above 12; for debt source: banks, other financial institutions, family/friends; for debt purpose: current consumption, fixed charges, durable goods, purchase of a house/flat, renovation, medical treatment, purchase/rent of working equipment, vacation, repayment of previous debts, business development, education/training and other.

² For example, the state described as MORTGAGE DEBTORS is characterized by probability of mortgage debt equal to 1, high probability of considerable value of debt but also a slight probability of other debt objectives, like durable goods purchase (0.116) or renovation (0.111).

³ Yes in the Table 1 represents significant role of a given factor in shaping membership probability in a given group of households on the financial market.

Additional information

Funding

This work was supported by the Seventh Framework Programme [FP7-PEOPLE COFUND no. 609402 –“2020 Researchers: Train 2 Move” (T2M)].