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).

Innovation districts: an investigation of the replication of the 22@ Barcelona’s Model in Boston

In 2000, the city of Barcelona launched 22@ Barcelona, dubbed the innovation district. The city sees the project as a means to accelerate Barcelona’s transition toward the knowledge economy. Other cities around the world have since followed the example of Barcelona, building or planning to build their own innovation districts. Boston began to establish its innovation district in 2010. Cities’ ultimate goal for these initiatives is to become more innovative and thus more competitive. Innovative districts are different from technology parks in that they aim to respond to a new economic paradigm in which economic production flows back to cities. The 22@ Barcelona model involves theoretical designs regarding five layers of innovation: economics, urban planning, productive, innovative, and creative. The comparative approach between 22@ Barcelona and Boston’s Innovation District intends to highlight the similarities and differences between those two innovation districts as well as providing a framework to define innovation districts.

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.