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Abstract
Economic theory often provides information on the variables to be included in economic
relationships (e.g., demands are functions of prices) and sometimes provides information on
the signs and magnitudes of first- and second-order derivatives (e.g., homogeneity and
concavity information). However, it rarely provides information concerning functional forms.
In the absence of this information, it is common to assume a specific functional form (e.g.,
translog) and subsume errors of approximation into a disturbance term. Unfortunately, the
estimated parameters of these approximating relationships do not consistently estimate the
economically-relevant characteristics of the true relationship unless the latter is of the
approximating class (White, 1980). Practical econometric solutions to the problem are now
becoming available. This paper discusses kernel regression (KR), flexible least squares
(FLS), generalized restricted least squares (GRLS) and latent class (LC) estimators. The
empirical performance of all four estimators is assessed using an artificially-generated data
set. Three of the estimators are then used to estimate characteristics of a labour demand
function for US agriculture.