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Full Information Estimation of Household Income Risk and Consumption Insurance

Author

Listed:
  • Arpita Chatterjee

    (UNSW Business School, UNSW)

  • James Morley

    (UNSW Business School, UNSW)

  • Aarti Singh

    (University of Sydney)

Abstract

We develop a panel unobserved components model of household income and consumption that can be estimated using full information methods. Maximum likelihood estimates for a simple version of this model suggests similar income risk, but higher consumption insurance relative to the partial information moments-based estimates in Blundell, Pistaferri, and Preston (2008) for the same panel dataset. Bayesian model comparison supports this simple version of the model that only allows a spillover from permanent income to permanent consumption, but assumes no cointegration and no persistence in transitory components. However, consumption insurance and income risk estimates are highly robust across different specifications.

Suggested Citation

  • Arpita Chatterjee & James Morley & Aarti Singh, 2017. "Full Information Estimation of Household Income Risk and Consumption Insurance," Discussion Papers 2017-07, School of Economics, The University of New South Wales.
    • Handle: RePEc:swe:wpaper:2017-07
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    File URL: http://research.economics.unsw.edu.au/RePEc/papers/2017-07.pdf
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    panel unobserved components; Bayesian model comparison; permanent income; household consumption behavior;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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