A0218
Title: A partially parametric model
Authors: Daniel Henderson - University of Alabama (United States) [presenting]
Christopher Parmeter - University of Miami (United States)
Abstract: A model is proposed which includes both a known (potentially) nonlinear parametric component and an unknown nonparametric component. This approach is feasible given that we estimate the finite sample parameter vector and the bandwidths simultaneously. We show that our objective function is asymptotically equivalent to the individual objective criteria for the parametric parameter vector and the nonparametric function. In the special case where the parametric component is linear in parameters, our single-step method is asymptotically equivalent to the two-step partially linear model estimator. Monte Carlo simulations support the asymptotic developments and show impressive finite sample performance. We apply our method to the case of a partially constant elasticity of substitution production function for an unbalanced sample of 134 countries from 1955-2011 and find that the parametric parameters are relatively stable for different nonparametric control variables in the full sample. However, we find substantial parameter heterogeneity between developed and developing countries which results in important differences when estimating the elasticity of substitution.
