In Bayesian analysis of dynamic stochastic general equilibrium (DSGE) models, prior distributions for some of the taste-and-technology parameters can be obtained from microeconometric or presample evidence, but it is difficult to elicit priors for the parameters that govern the law of motion of...

A necessary condition - the Principle of Fermat-Lagrange - is offered for mixed smooth-convexoptimization problems. This generalizes and unifies most of the known necessary conditions forconcrete finite and infinite dimensional optimization problems of interest. The new idea in comparisonwith...

Efficient influence functions and efficient score functions are representations of derivatives of maps. The invariance of theformer under changes of parametrization, and the formula relating the efficient influence function for the projection of aparameter to the efficient score function, turn...

In this paper we introduce several classes of generalized convexfunctions already discussed in the literature and show the relationbetween those function classes. Moreover, for some of those functionclasses a Farkas-type theorem is proved. As such this paper unifiesand extends results existing...

We consider the univariate two-scale refinement equation.The paper analyzes the correlation between the existence of smoothcompactly supported solutions of this equation and the convergence ofthe corresponding cascade algorithm/subdivision scheme. We introducea criterion that expresses this...

We consider infinite products of the form ,where {mk} is an arbitrary sequence of trigonometric polynomials of degree at most n with uniformly bounded normssuch that mk(0)=1 for all k. We show that can decrease at infinity not faster than and present conditions underwhich this maximal decay...

General equilibrium models are typically presented with mathematical methods, such as the Edgeworth Box, that do not easily generalize to more than two goods and more than two agents. This is fine as a conceptual introduction, but it may be insufficient in the "Big Data-Machine-Learning-Era",...