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

• Julio López - Laborda ^[1] ; Carmen Marín González ; Jorge Onrubia ^[2]
  1. [1] Universidad de Zaragoza

     Universidad de Zaragoza

     Zaragoza, España

     
  2. [2] Universidad Complutense de Madrid

     Universidad Complutense de Madrid

     Madrid, España

     
• Localización: Documentos de trabajo ( FEDEA ), ISSN 1696-7496, Nº. 15, 2017, págs. 1-19
• Idioma: inglés
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• Resumen

  • 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.