2006
DOI: 10.1002/jae.863
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Estimates of semiparametric equivalence scales

Abstract: SUMMARYWithin the semiparametric framework introduced by Pendakur (1999) we introduce a new loss function to estimate equivalence scales. This loss function uses all available information from the total expenditures of both the reference and nonreference households and as such it produces more reliable estimates. Using Canadian family expenditure data for 1996 we apply this loss function to obtain equivalence scale estimates for a variety of expenditure share categories such as food, fuel and clothing.

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Cited by 12 publications
(19 citation statements)
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“…This means that the distributions of equivalent expenditure-that is, x for the nonreference and x υ z for the reference household-must be assumed to overlap if one is to use semiparametric methods to identify equivalence scales. Stengos and Wang (2002) note that  1 will always find minima where υ z is set such that either or both of O f x, z and O f x υ z are close to zero. This is because in this case,  1 gets close to zero no matter how well or poorly the estimated regression curves fit each other.…”
Section: Semiparametric Estimation Given Aesementioning
confidence: 98%
“…This means that the distributions of equivalent expenditure-that is, x for the nonreference and x υ z for the reference household-must be assumed to overlap if one is to use semiparametric methods to identify equivalence scales. Stengos and Wang (2002) note that  1 will always find minima where υ z is set such that either or both of O f x, z and O f x υ z are close to zero. This is because in this case,  1 gets close to zero no matter how well or poorly the estimated regression curves fit each other.…”
Section: Semiparametric Estimation Given Aesementioning
confidence: 98%
“…Pendakur, 1999, Stengos et al, 2006) is based on nonparametric estimates of Engel curves. The log equivalence scale is estimated as the horizontal shift of , say, the Engel curve of singles, in order to make it lie on the Engel curve of couples.…”
Section: Nonparametric Analysismentioning
confidence: 99%
“…Using the classic approach of Engel (1895) as a starting point, we cover the modern methodological developments in the field. These include extensions of the Linear Expenditure System (Lluch 1973;Howe et al 1979), which have often been applied to German expenditure data; the quadratic extension (QAI) (Banks et al 1997) of the influential Almost Ideal Demand System (AI) (Deaton and Muellbauer 1980a), which is now the standard approach for modeling household demand; semiparametric approaches (Pendakur 1999;Stengos et al 2006); and nonparametric approaches based on the counterfactual framework (Szulc 2009;Dudel 2015). These methods roughly span a continuum in terms of model complexity, data requirements, and the restrictiveness of the underlying assumptions.…”
Section: Introductionmentioning
confidence: 99%