Abstract
In this contribution, we explore the sensitivity of parameter estimates derived through the generalized maximum entropy (GME) approach under alternative specifications of the width of the error term supports. Although many recommend a “three-sigma” rule for setting the width of this term, there can be noticeable differences in the results if it is expanded beyond that, as others in the literature have suggested. We use a Monte Carlo analysis to see how imposing a moment-based condition into the GME problem, as an additional constraint, affects the results. We find that it removes the sensitivity of the parameter estimates to the width of the supports for the error term and that this remains robust even when the data is ill-conditioned. Based on this, we recommend that researchers impose this condition when doing GME-based estimation, to improve the performance of the estimator.
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Notes
- 1.
Preckel refers to a “reference distribution” when describing the support space, since he is using the cross-entropy formulation to motivate the similarity between GME and the least-squares estimation procedure, in terms of minimizing deviations. However, the “reference” distribution actually used in his discussion is uniform, which makes the Generalized Cross-Entropy (GCE) minimization problem equivalent to the primal GME maximization problem that we use for our discussion, here. So we will avoid confusion of terminology by solely referring to the support space of the parameter—which Preckel suggests should be chosen by placing the OLS estimate at its center and placing values symmetrically on either side that are equal to multiples of the standard error of the OLS estimate (pp. 370 & 373).
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Appendix
Appendix
General Derivation for Basic GME Problem
We can write the GME problem as
which can be rewritten as
Which give the first-order conditions
taking the k = 1 and k = 2 K + 1 cases, we can derive the result \(\lambda_{j} = \frac{1}{{z_{1}^{e} - z_{2K + 1}^{e} }}\ln \left( {\frac{{p_{j,2K + 1}^{e} }}{{p_{j,1}^{e} }}} \right)\) and substitute it into the following expression derived from the FOCs for \(p_{i}^{b}\)
And we obtain \(\frac{{p_{{i^{\prime}}}^{b} }}{{p_{i}^{b} }} = \left[ {\prod\limits_{j = 1}^{N} {\left( {\frac{{p_{j,2K + 1}^{e} }}{{p_{j,1}^{e} }}} \right)^{{x_{j} }} } } \right]^{{\frac{{z_{i}^{b} - z_{{i^{\prime}}}^{b} }}{{z_{1}^{e} - z_{2K + 1}^{e} }}}} \quad \forall i,i^{\prime} \in {\mathbf{I}}\;\quad {\text{and}}\quad I + 1 \notin {\mathbf{I}}\).
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Howitt, R.E., Msangi, S. (2019). Using Moment Constraints in GME Estimation. In: Msangi, S., MacEwan, D. (eds) Applied Methods for Agriculture and Natural Resource Management. Natural Resource Management and Policy, vol 50. Springer, Cham. https://doi.org/10.1007/978-3-030-13487-7_8
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