7,536 research outputs found
Yield and Area Elasticities. A Cost Function Approach with Uncertainty
This paper develops a method to jointly estimate crop yield elasticities and area elasticities with respect to output prices based on a theoretically consistent model. The model uses a duality theory approach for the multi-output and multi-input firm, and introduces uncertainty in the level of target output which conditions the cost minimization problem, in the output prices and in the conditional input demand functions. The underlying production technology is conditioned on fixed inputs, both allocatable and non-allocatable. Up to our knowledge, there have been no theoretical developments of this type of models for multioutput technologies. Our approach is also novel because no previous model of this type has introduced the effects of allocatable fixed inputs. We provide an empirical application of this theoretical framework using State-level data and approximating the dual cost function by a normalized quadratic flexible functional form. We derive expressions for the elasticities of interest conditional on the function specification assumed.yield elasticities, area elasticities, duality theory, cost function, uncertainty, Production Economics,
Beyond conventional factorization: Non-Hermitian Hamiltonians with radial oscillator spectrum
The eigenvalue problem of the spherically symmetric oscillator Hamiltonian is
revisited in the context of canonical raising and lowering operators. The
Hamiltonian is then factorized in terms of two not mutually adjoint factorizing
operators which, in turn, give rise to a non-Hermitian radial Hamiltonian. The
set of eigenvalues of this new Hamiltonian is exactly the same as the energy
spectrum of the radial oscillator and the new square-integrable eigenfunctions
are complex Darboux-deformations of the associated Laguerre polynomials.Comment: 13 pages, 7 figure
Understanding interdependency through complex information sharing
The interactions between three or more random variables are often nontrivial,
poorly understood, and yet, are paramount for future advances in fields such as
network information theory, neuroscience, genetics and many others. In this
work, we propose to analyze these interactions as different modes of
information sharing. Towards this end, we introduce a novel axiomatic framework
for decomposing the joint entropy, which characterizes the various ways in
which random variables can share information. The key contribution of our
framework is to distinguish between interdependencies where the information is
shared redundantly, and synergistic interdependencies where the sharing
structure exists in the whole but not between the parts. We show that our
axioms determine unique formulas for all the terms of the proposed
decomposition for a number of cases of interest. Moreover, we show how these
results can be applied to several network information theory problems,
providing a more intuitive understanding of their fundamental limits.Comment: 39 pages, 4 figure
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