Encouraging the implication of shops in the city by means of retail associationism. The case of Bilbao

[EN] Retail activity in urban areas constitutes a key variable in the health of a city. For that reason, the processes of urban revitalization and retail revitalization run in parallel manner. Integrated management models for urban centres constitute a good framework to harness the competitiveness of the cities and their retail businesses, but they require of all implied participation, by means of a public – private cooperation.

Data Revisions in the Estimation of DSGE Models

Revisions of US macroeconomic data are not white-noise. They are persistent, correlated with real-time data, and with high variability (around 80% of volatility observed in US real-time data). Their business cycle effects are examined in an estimated DSGE model extended with both real-time and final data. After implementing a Bayesian estimation approach, the role of both habit formation and price indexation fall significantly in the extended model. The results show how revision shocks of both output and inflation are expansionary because they occur when real-time published data are too low and the Fed reacts by cutting interest rates. Consumption revisions, by contrast, are countercyclical as consumption habits mirror the observed reduction in real-time consumption. In turn, revisions of the three variables explain 9.3% of changes of output in its long-run variance decomposition.

Time Variation in an Optimal Asymmetric Preference Monetary Policy Model

This paper considers a time varying parameter extension of the Ruge-Murcia (2003, 2004) model to explore whether some of the variation in parameter estimates seen in the literature could arise from this source. A time varying value for the unemployment volatility parameter can be motivated through several means including variation in the slope of the Phillips curve or variation in the preferences of the monetary authority.We show that allowing time variation for the coefficient on the unemployment volatility parameter improves the model fit and it helps to provide an explanation of inflation bias based on asymmetric central banker preferences, which is consistent across subsamples.

Can the change in the composition of the US GDP explain the Great Moderation? A test via oil price shocks

The paper investigates whether the growing GDP share of the services sector can contribute to explain the great moderation in the US. We identify and analyze three oil price shocks and use a SVAR analysis to measure their economic impact on the US economy at both the aggregate and the sectoral level. We find mixed support for the explanation of the great moderation in terms of shrinking oil shock volatilities and observe that increases (decreases) in oil shock volatilities are contrasted by a weakening (strengthening) in their transmission mechanism. Across sectors, services are the least affected by any oil shock. As the contribution of services to the GDP volatility increases over time, we conclude that a composition effect contributed to moderate the conditional volatility to oil shocks of the US GDP.

Optimal Monetary Policy with Asymmetric Preferences for Output

Using a model of an optimizing monetary authority which has preferences that weigh inflation and unemployment, Ruge-Murcia (2003, 2004) finds empirical evidence that the authority has asymmetric preferences for unemployment. We extend this model to weigh inflation and output and show that the empirical evidence using these series also supports an asymmetric preference hypothesis, only in our case, preferences are asymmetric for output. We also find evidence that the monetary authority targets potential output rather than some higher output level as would be the case in an extended Barro and Gordon (1983) model.

Inability of Escherichia coli to Resuscitate from the Viable but Nonculturable State

Poster presentado 10th Symposium on Aquatic Microbial Ecology (SAME10) september 2-7 2007, Faro

Squaring the circle. Social and environmental implications of pre-pottery neolithic building technology at Tell Qarassa (South Syria)

We present the results of the microstratigraphic, phytolith and wood charcoal study of the remains of a 10.5 ka roof. The roof is part of a building excavated at Tell Qarassa (South Syria), assigned to the Pre-Pottery Neolithic B period (PPNB). The Pre-Pottery Neolithic (PPN) period in the Levant coincides with the emergence of farming. This fundamental change in subsistence strategy implied the shift from mobile to settled aggregated life, and from tents and huts to hard buildings. As settled life spread across the Levant, a generalised transition from round to square buildings occurred, that is a trademark of the PPNB period. The study of these buildings is fundamental for the understanding of the ever-stronger reciprocal socio-ecological relationship humans developed with the local environment since the introduction of sedentism and domestication. Descriptions of buildings in PPN archaeological contexts are usually restricted to the macroscopic observation of wooden elements (posts and beams) and mineral components (daub, plaster and stone elements). Reconstructions of microscopic and organic components are frequently based on ethnographic analogy. The direct study of macroscopic and microscopic, organic and mineral, building components performed at Tell Qarassa provides new insights on building conception, maintenance, use and destruction. These elements reflect new emerging paradigms in the relationship between Neolithic societies and the environment. A square building was possibly covered here with a radial roof, providing a glance into a topologic shift in the conception and understanding of volumes, from round-based to square-based geometries. Macroscopic and microscopic roof components indicate buildings were conceived for year-round residence rather than seasonal mobility. This implied performing maintenance and restoration of partially damaged buildings, as well as their adaptation to seasonal variability

Long memory in return structures from developed markets

[En]The present study aimed at investigating the existence of long memory properties in ten developed stock markets across the globe. When return series exhibit long memory, the series realizations are not independent over time and past returns can help predict future returns, thus violating the market efficiency hypothesis. It poses a serious challenge to the supporters of random walk behavior of the stock returns indicating a potentially predictable component in the series dynamics. We computed Hurst-Mandelbrot’s Classical R/S statistic, Lo’s statistic and semi parametric GPH statistic using spectral regression. The findings suggest existence of long memory in volatility and random walk for logarithmic return series in general for all the selected stock market indices. Findings are in line with the stylized facts of financial time series.

Efficient learning of decomposable models with a bounded clique size

The learning of probability distributions from data is a ubiquitous problem in the fields of Statistics and Artificial Intelligence. During the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models due to their advantageous theoretical properties. Some of these algorithms can be used to search for a maximum likelihood decomposable model with a given maximum clique size, k, which controls the complexity of the model. Unfortunately, the problem of learning a maximum likelihood decomposable model given a maximum clique size is NP-hard for k > 2. In this work, we propose a family of algorithms which approximates this problem with a computational complexity of O(k · n^2 log n) in the worst case, where n is the number of implied random variables. The structures of the decomposable models that solve the maximum likelihood problem are called maximal k-order decomposable graphs. Our proposals, called fractal trees, construct a sequence of maximal i-order decomposable graphs, for i = 2, ..., k, in k − 1 steps. At each step, the algorithms follow a divide-and-conquer strategy based on the particular features of this type of structures. Additionally, we propose a prune-and-graft procedure which transforms a maximal k-order decomposable graph into another one, increasing its likelihood. We have implemented two particular fractal tree algorithms called parallel fractal tree and sequential fractal tree. These algorithms can be considered a natural extension of Chow and Liu’s algorithm, from k = 2 to arbitrary values of k. Both algorithms have been compared against other efficient approaches in artificial and real domains, and they have shown a competitive behavior to deal with the maximum likelihood problem. Due to their low computational complexity they are especially recommended to deal with high dimensional domains.

On the Benefits of Including Age-structure in Harvest Control Rules

This paper explores the benefits of including age-structure in the control rule (HCR) when decision makers regard their (age-structured) models as approximations. We find that introducing age structure into the HCR reduces both the volatility of the spawning biomass and the yield. Although at a fairly imprecise level the benefits are lower, there are still major advantages for actual assessment precision of the case study. Moreover, we find that when age-structure is included in the HCR the relative ranking of different policies in terms of variance in biomass and yield does not differ. These results are shown both theoretically and numerically by applying the model to the Southern Hake fishery.

Lessons from History for Transitions to a Low Carbon Economy

4 p.

Evaluation of two alternative carbon capture and storage technologies

4 p.

The Slow Search for Solutions: Lessons from Historical Energy Transitions by Sector and Service

28 p.

Optimal Abandonment of Coal-Fired Stations in the EU

36 p.

The DICER Model: Methodological Issues and Initial Results

31 p.