Solving the non-convexity problem in some shopping-time and human-capital models

Several works in the shopping-time and in the human-capital literature, due to the nonconcavity of the underlying Hamiltonian, use ��rst-order conditions in dynamic optimization to characterize necessity, but not su�� ciency, in intertemporal problems. In this work I choose one paper in each one of these two areas and show that optimality can be characterized by means of a simple aplication of Arrow��s (1968) su�� ciency theorem.

Endogenous time-dependent rules and inflation inertia: preliminary version

When policy rules are changed, the effect of nominal rigidities should be modelled through endogenous pricing rules. We endogenize Taylor (1979) type pricing rule to examine the output effects of monetary disinflations. We derive optimal fixed-price time-dependent rules in inflationary steady states and during disinflations. We also develop a methodology to aggregate individual pricing rules which vary through disinflation. This allows us to reevaluate the output costs of monetary disinflation, including aspects as the role of the initial leveI of inflation and the importance of the degree of credibility of the policy change.

The time consistency of the Friedman rule

We consider the problem of time consistency of the Ramsey monetary and fiscal policies in an economy without capital. Following Lucas and Stokey (1983) we allow the government at date t to leave its successor at t + 1 a profile of real and nominal debt of all maturities, as a way to influence its decisions. We show that the Ramsey policies are time consistent if and only if the Friedman rule is the optimal Ramsey policy.

Non-asymptotic confidence bounds for the optimal value of a stochastic program

We discuss a general approach to building non-asymptotic confidence bounds for stochastic optimization problems. Our principal contribution is the observation that a Sample Average Approximation of a problem supplies upper and lower bounds for the optimal value of the problem which are essentially better than the quality of the corresponding optimal solutions. At the same time, such bounds are more reliable than ���standard��� confidence bounds obtained through the asymptotic approach. We also discuss bounding the optimal value of MinMax Stochastic Optimization and stochastically constrained problems. We conclude with a small simulation study illustrating the numerical behavior of the proposed bounds.