Estimation of Coherent Demand Systems with Many Binding Non-Negativity Constraints
Mark M. Pitt and Daniel L. Millimet
Two econometric issues arise in the estimation of complete systems of
producer or consumer demands when many non-negativity constraints are
binding for a large share of observations, as frequently occurs with
micro-level data. The first is computational.
The econometric model is essentially an endogenous switching regimes model which requires the evaluation of multivariate
probability integrals. The second is the relationship between demand theory and statistical coherency. If the indirect utility or cost function
underlying the demand system does not satisfy the regularity conditions at each observation, the likelihood is incoherent in that the sum of the prob-
abilities for all demand regimes is not unity and maximum likelihood estimates are inconsistent.
The solution presented is to use the Gibbs Sampling technique and data augmentation algorithm
and rejection sampling, to solve both the dimensionality and coherency problem. With rejection
sampling one can straightforwardly impose only the necessary conditions for coherency, coherency
at each data point rather than global coherency. The method is illustrated with a series of simulated
demand systems derived from the translog indirect random utility function. The results highlight
the importance of imposing regularity when there are many non-consumed goods and the gains
from imposing such conditions locally rather than globally.
1999 Working Papers Page
This page last updated on: March 11, 1999