Bounds for the probability distribution function of the linear ACD process

This paper derives both lower and upper bounds for the probability distribution function of stationary ACD(p, q) processes. For the purpose of illustration, I specialize the results to the main parent distributions in duration analysis. Simulations show that the lower bound is much tighter than the upper bound.

Sobre o sistema de amortização linear crescente

Aiming to reach a compromise solution to the issues of default risk and the payment capacity of takers of housing loans, Jorge Oscar de Mello Flôres submited to the Banco Nacional de Habitação, which was then in charge of the Brazilian System of Housing Financing, what he named as the Linearly Increasing System of Amortization. (LISA). Following a critical analysis of the LISA, it is proposed the alternative named as the Generalyzed System of Mixed Amortization (GSMA).

Multiple structural change models:a simulation analysis

In a reccnt paper. Bai and Perron (1998) considcrccl theoretical issues relatec\ lo lhe limiting distriblltion of estimators and test. statist.ics in the linear model \\'ith multiplc struct ural changes. \Ve assess. via simulations, the adequacy of the \'arious I1Iethods suggested. These CO\'er the size and power of tests for structural changes. the cO\'erage rates of the confidence Íntervals for the break dates and the relat.Í\'e merits of methods to select the I1umber of breaks. The \'arious data generating processes considered alIo,,' for general conditions OIl the data and the errors including differellces across segmcll(s. Yarious practical recommendations are made.

How children's television networks in the United States have adapted to the changing digital habits of their young audiences: A comparative analysis between the Disney Channel and Nickelodeon

In the contemporary societies, many children are drawn to digital media, using it in ways that were initially unfathomable. Changing digital habits among young children have been affiliated to the rapid development, witnessed in the technological field. Prevalently, new forms of technology are being developed and ingrained into young children’s day-to-day activities. The emergence of new forms of technology has in turn prompted significant changes in digital and media consumption particularly, among young children. Changes in media and digital consumption have in turn instigated linear transition in the analogue media industries. This has resulted in analogue media networks working towards digitalizing their industries in a manner that will befit changing digital habits among young children. This report aims at establishing and analyzing the different ways in which children’s digital habits have changed and revolutionized. To achieve this, the report will critically examine the existing scope of knowledge, with reference to changing digital habits among young audiences. Further, the report also aims at establishing the manner in which children television networks have adapted to the changing digital habits among young audiences. To achieve this, the report will focus on two children television networks, Disney channel, and Nickelodeon. After which, a comparative analysis will be conducted to establish the changes made by each of these television channels, with the aim of adapting to the new digital habits among children.

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.