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Modelling household finances: A Bayesian approach to a multivariate two-part model

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  • Brown, Sarah
  • Ghosh, Pulak
  • Su, Li
  • Taylor, Karl

Abstract

We contribute to the empirical literature on household finances by introducing a Bayesian multivariate two-part model, which has been developed to further our understanding of household finances. Our flexible approach allows for the potential interdependence between the holding of assets and liabilities at the household level and also encompasses a two-part process to allow for differences in the influences on asset or liability holding and on the respective amounts held. Furthermore, the framework is dynamic in order to allow for persistence in household finances over time. Our findings endorse the joint modelling approach and provide evidence supporting the importance of dynamics. In addition, we find that certain independent variables exert different influences on the binary and continuous parts of the model thereby highlighting the flexibility of our framework and revealing a detailed picture of the nature of household finances.

Suggested Citation

  • Brown, Sarah & Ghosh, Pulak & Su, Li & Taylor, Karl, 2015. "Modelling household finances: A Bayesian approach to a multivariate two-part model," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 190-207.
    • Handle: RePEc:eee:empfin:v:33:y:2015:i:c:p:190-207
    DOI: 10.1016/j.jempfin.2015.03.017
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    Cited by:

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    2. Sarah Brown & Pulak Ghosh & Bhuvanesh Pareek & Karl Taylor, 2017. "Financial Hardship and Saving Behaviour: Bayesian Analysis of British Panel Data," Working Papers 2017011, The University of Sheffield, Department of Economics.
    3. Amel Attour & Marco Baudino & Jackie Krafft & Nathalie Lazaric, 2020. "Determinants of smart energy tracking application use at the city level: Evidence from France," Post-Print hal-02942483, HAL.
    4. Dittmann Iwona, 2016. "Rates of Return on Open-End Debt Investment Funds and Bank Deposits in Poland in the Years 1995–2015 – A Comparative Analysis," Folia Oeconomica Stetinensia, Sciendo, vol. 16(1), pages 93-112, December.
    5. Attour, Amel & Baudino, Marco & Krafft, Jackie & Lazaric, Nathalie, 2020. "Determinants of energy tracking application use at the city level: Evidence from France," Energy Policy, Elsevier, vol. 147(C).
    6. Feng, Xiangnan & Lu, Bin & Song, Xinyuan & Ma, Shuang, 2019. "Financial literacy and household finances: A Bayesian two-part latent variable modeling approach," Journal of Empirical Finance, Elsevier, vol. 51(C), pages 119-137.
    7. Bucciol, Alessandro & Miniaci, Raffaele & Pastorello, Sergio, 2017. "Return expectations and risk aversion heterogeneity in household portfolios," Journal of Empirical Finance, Elsevier, vol. 40(C), pages 201-219.
    8. Brown, Sarah & Ghosh, Pulak & Pareek, Bhuvanesh & Taylor, Karl, 2021. "The protective role of saving: Bayesian analysis of British panel data," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 57-72.
    9. Jayabrata Biswas & Kiranmoy Das, 2021. "A Bayesian quantile regression approach to multivariate semi-continuous longitudinal data," Computational Statistics, Springer, vol. 36(1), pages 241-260, March.

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    More about this item

    Keywords

    Assets; Bayesian approach; Bridge distribution; Debt; Two-part model;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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