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Estimating Engel curves: A new way to improve the SILC-HBS matching process


  • Julio López-Laborda
  • Carmen Marín-González
  • Jorge Onrubia


There are several ways to match SILC-HBS surveys, with the most common technique involving the estimation of Engel curves using Ordinary Least Squares in logs with HBS data to impute household expenditure in the income dataset (SILC). The estimation in logs has certain advantages, as it can deal with skewness in data and reduce heteroskedasticity. However, the model needs to be corrected with a smearing estimate to retransform the results into levels. The presence of intrinsic heteroskedasticity in household expenditure therefore calls for another technique, as the smearing estimate produces a bias. Generalized Linear Models (GLMs) are presented as the best option.

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  • Julio López-Laborda & Carmen Marín-González & Jorge Onrubia, 2017. "Estimating Engel curves: A new way to improve the SILC-HBS matching process," Working Papers 2017-15, FEDEA.
  • Handle: RePEc:fda:fdaddt:2017-15

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    Cited by:

    1. Lucía Gorjón & Sara de la Rica & Antonio Villar, 2018. "The social cost of unemployment: the Spanish labour market from a social welfare approach," Working Papers 18.11, Universidad Pablo de Olavide, Department of Economics.
    2. Gorjón, Lucía & de la Rica, Sara & Villar, Antonio, 2018. "The Social Cost of Unemployment: The Spanish Labour Market from a Social Welfare Approach," IZA Discussion Papers 11850, Institute for the Study of Labor (IZA).

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