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).

Mapping digital media: Brazil

FGV Direito Rio

Mapping digital media: Brazil

The impact of digitization was felt before it could be described and explained. The Mapping Digital Media project is a way of catching up, an ambitious attempt at depicting and understanding the progress and effects of digitization on media and communications systems across the world. The publication of over 50 country reports provides the most comprehensive picture to date on the changes undergone by journalism, news production, and the media as a result of the transition of broadcasting from analog to digital and the advent of the internet. These extensive reports, all sharing the same structure, cover issues such as media consumption, public media, changes in journalism, digital activism, new regulation, and business models. Reports have been published from nine Latin American countries: Mexico, Argentina, Colombia, Peru, Chile, Brazil, Guatemala, Nicaragua, and Uruguay. Given the recent evolution of Brazil’s media landscape and regulation, and its position as a regional reference, few reports have generated as much expectation as the Brazilian one. This excellent text is key to understanding digitization in Brazil, in Latin America, and in the world at large.

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.

Modified value stream mapping of airport check-in procedures

In June 2014 Brazil hosted the FIFA World Cup and in August 2016 Rio de Janeiro hosts the Summer Olympics. These two seminal sporting events will draw tens of thousands of air travelers through Brazil’s airports, airports that are currently in the midst of a national modernization program to address years of infrastructure neglect and insufficient capacity. Raising Brazil’s major airports up to the standards air travelers experience at major airports elsewhere in the world is more than just a case of building or remodeling facilities, processes must also be examined and reworked to enhance traveler experience and satisfaction. This research paper examines the key interface between airports and airline passengers—airport check-in procedures—according to how much value and waste there is associated with them. In particular, the paper makes use of a value stream mapping construct for services proposed by Martins, Cantanhede, and Jardim (2010). The uniqueness of this construct is that it attributes each activity with a certain percentage and magnitude of value or waste which can then be ordered and prioritized for improvement. Working against a fairly commonly expressed notion in Brazil that Brazil’s airports are inferior to the airports of economically advanced countries, the paper examines Rio’s two major airports, Galeão International and Santos Dumont in comparison to Washington D.C.’s Washington National and Dulles International airports. The paper seeks to accomplish three goals: - Determine whether there are differences in airport passenger check-in procedures between U.S. and Brazilian airports in terms of passenger value - Present options for Brazilian government or private sector authorities to consider adopting or implementing at Brazilian airports to maximize passenger value - Validate the Martins et al. construct for use in evaluating the airport check-in procedures Observations and analysis proved surprising in that all airports and service providers follow essentially the same check-in processes but execute them differently yet still result in similar overall performance in terms of value and waste. Although only a few activities are categorized as completely wasteful (and therefore removed in the revised value stream map of check-in activities), the weighting and categorization of individual activities according to their value (or waste) presents decision-makers a means to prioritize possible corrective actions. Various overall recommendations are presented based on this analysis. Most importantly, this paper demonstrates the viability of using the construct developed by Martins et al to examine airport operations, as well as its applicability to the study of other service industry processes.

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.