Convex combinations of long memory estimates from different sampling rates

Convex combinations of long memory estimates using the same data observed at different sampling rates can decrease the standard deviation of the estimates, at the cost of inducing a slight bias. The convex combination of such estimates requires a preliminary correction for the bias observed at lower sampling rates, reported by Souza and Smith (2002). Through Monte Carlo simulations, we investigate the bias and the standard deviation of the combined estimates, as well as the root mean squared error (RMSE), which takes both into account. While comparing the results of standard methods and their combined versions, the latter achieve lower RMSE, for the two semi-parametric estimators under study (by about 30% on average for ARFIMA(0,d,0) series).

Endogenous normative indices for the industry concentration

This paper presents new indices for measuring the industry concentration. The indices proposed (C n ) are of a normative type because they embody (endogenous) weights matching the market shares of the individual firms to their Marshallian welfare shares. These indices belong to an enlarged class of the Performance Gradient Indexes introduced by Dansby&Willig(I979). The definition of Cn for the consumers allows a new interpretation for the Hirschman-Herfindahl index (H), which can be viewed as a normative index according to particular values of the demand parameters. For homogeneous product industries, Cn equates H for every market distribution if (and only if) the market demand is linear. Whenever the inverse demand curve is convex (concave), H underestimates( overestimates) the industry concentration measured by the normative indexo For these industries, H overestimates (underestimates) the concentration changes caused by market transfers among small firms if the inverse demand curve is convex(concave) and underestimates( overestimates) it when such tranfers benefit a large firm, according to the convexity (or the concavity) of the demand curve. For heterogeneous product industries, an explicit normative index is obtained with a market demand derived from a quasi-linear utilility function. Under symmetric preferences among the goods, the index Cn is always greater than or equal the H-index. Under asymmetric assumptions, discrepancies between the firms' market distribution and the differentiationj substitution distributions among the goods, increase the concentration but make room for some horizontal mergers do reduce it. In particular, a mean preserving spread of the differentiation(substitution) increases(decreases) the concentration only if the smaller firms' goods become more(less) differentiated(substitute) w.r.t. the other goods. One important consequence of these results is that the consumers are benefitted when the smaller firms are producing weak substitute goods, and the larger firms produce strong substitute goods or face demand curves weakly sensitive to their own prices.

Integral representation with convex capacities that are squeeze of (additive) probability measures

In this paper I will investigate the conditions under which a convex capacity (or a non-additive probability which exhibts uncertainty aversion) can be represented as a squeeze of a(n) (additive) probability measure associate to an uncertainty aversion function. Then I will present two alternatives forrnulations of the Choquet integral (and I will extend these forrnulations to the Choquet expected utility) in a parametric approach that will enable me to do comparative static exercises over the uncertainty aversion function in an easy way.

Convergence analysis of sampling-based decomposition methods for risk-averse multistage stochastic convex programs

We consider a class of sampling-based decomposition methods to solve risk-averse multistage stochastic convex programs. We prove a formula for the computation of the cuts necessary to build the outer linearizations of the recourse functions. This formula can be used to obtain an efficient implementation of Stochastic Dual Dynamic Programming applied to convex nonlinear problems. We prove the almost sure convergence of these decomposition methods when the relatively complete recourse assumption holds. We also prove the almost sure convergence of these algorithms when applied to risk-averse multistage stochastic linear programs that do not satisfy the relatively complete recourse assumption. The analysis is first done assuming the underlying stochastic process is interstage independent and discrete, with a finite set of possible realizations at each stage. We then indicate two ways of extending the methods and convergence analysis to the case when the process is interstage dependent.

Multistep stochastic mirror descent for risk-averse convex stochastic programs based on extended polyhedral risk measures

We consider risk-averse convex stochastic programs expressed in terms of extended polyhedral risk measures. We derive computable con dence intervals on the optimal value of such stochastic programs using the Robust Stochastic Approximation and the Stochastic Mirror Descent (SMD) algorithms. When the objective functions are uniformly convex, we also propose a multistep extension of the Stochastic Mirror Descent algorithm and obtain con dence intervals on both the optimal values and optimal solutions. Numerical simulations show that our con dence intervals are much less conservative and are quicker to compute than previously obtained con dence intervals for SMD and that the multistep Stochastic Mirror Descent algorithm can obtain a good approximate solution much quicker than its nonmultistep counterpart. Our con dence intervals are also more reliable than asymptotic con dence intervals when the sample size is not much larger than the problem size.