Bayesian Estimation of a Simple Macroeconomic Model for a Small Open and Partially Dollarized Economy

I describe a simple new-keynesian macroeconomic model for a small open and partially dollarized economy, which closely resembles the Quarterly Projection Model (QPM) developed at the Central Bank of Peru (Vega et al. (2009)). Then I use Bayesian techniques and quarterly data from Peru to estimate a large group of parameters. The empirical findings provide support for some of the parameters values imposed in the original QPM. In contrast, I find that another group of coefficients – e.g., the weights on the forward-looking components in the aggregate demand and the Phillips curve equations, among several others – should be modified to be more consistent with the data. Furthermore, the results validate the operation of different channels of monetary policy transmission, such as the traditional interest rate channel and the exchange rate channel. I also find evidence that in the most recent part of the sample (2004 onwards), the expectations channel has become more prominent, as implied by the estimated values of the forward-looking parameters in the aggregate demand and the Phillips curve equations.Monetary Policy; Partial Dollarization; Bayesian Estimation

¿Qué explica las fluctuaciones de la inflación en el Perú en el periodo 2002-2008? Evidencia de un análisis VAR estructural

En este trabajo se realiza un ejercicio de descomposición histórica de la inflación peruana a partir de un modelo VAR estructural identificado con restricciones de largo plazo. Mediante este ejercicio se estima la contribución de distintas fuentes de choques de origen doméstico y externo a la evolución observada de la inflación desde la adopción del régimen de Metas de Inflación en el Perú (2002) hasta el segundo trimestre de 2008. Los resultados indican que los choques domésticos de demanda han sido los más relevantes en la primera parte de la muestra (hasta 2005, aproximadamente), mientras que posteriormente los choques de origen foráneo – asociados a la inflación externa y a los términos de intercambio – han conducido en mayor medida las fluctuaciones inflacionarias.

VisualUVAM: A Decision Support System Addressing the Curse of Dimensionality for the Multi-Scale Assessment of Urban Vulnerability in Spain

[EN] Many-objective optimization methods have proven successful in the integration of research attributes demanded for urban vulnerability assessment models. However, these techniques suffer from the curse of the dimensionality problem, producing an excessive burden in the decision-making process by compelling decision-makers to select alternatives among a large number of candidates. In other fields, this problem has been alleviated through cluster analysis, but there is still a lack in the application of such methods for urban vulnerability assessment purposes. This work addresses this gap by a novel combination of visual analytics and cluster analysis, enabling the decision-maker to select the set of indicators best representing urban vulnerability accordingly to three criteria: expert¿s preferences, goodness of fit, and robustness. Based on an assessment framework previously developed, VisualUVAM affords an evaluation of urban vulnerability in Spain at regional, provincial, and municipal scales, whose results demonstrate the effect of the governmental structure of a territory over the vulnerability of the assessed entities.This research was funded by the Spanish Ministry of Economy and Competitiveness, along with FEDER, grant number Project: BIA2017-85098-R".Salas, J.; Yepes, V. (2019). VisualUVAM: A Decision Support System Addressing the Curse of Dimensionality for the Multi-Scale Assessment of Urban Vulnerability in Spain. Sustainability. 11(8):2191-01-2191-17. https://doi.org/10.3390/su11082191S2191-012191-17118Rigillo, M., & Cervelli, E. (2014). Mapping Urban Vulnerability: The Case Study of Gran Santo Domingo, Dominican Republic. Advanced Engineering Forum, 11, 142-148. doi:10.4028/www.scientific.net/aef.11.142Malekpour, S., Brown, R. R., & de Haan, F. J. (2015). Strategic planning of urban infrastructure for environmental sustainability: Understanding the past to intervene for the future. Cities, 46, 67-75. doi:10.1016/j.cities.2015.05.003Salas, J., & Yepes, V. (2018). Urban vulnerability assessment: Advances from the strategic planning outlook. Journal of Cleaner Production, 179, 544-558. doi:10.1016/j.jclepro.2018.01.088Moraci, F., Errigo, M., Fazia, C., Burgio, G., & Foresta, S. (2018). Making Less Vulnerable Cities: Resilience as a New Paradigm of Smart Planning. Sustainability, 10(3), 755. doi:10.3390/su10030755De Gregorio Hurtado, S. (2017). Is EU urban policy transforming urban regeneration in Spain? Answers from an analysis of the Iniciativa Urbana (2007–2013). Cities, 60, 402-414. doi:10.1016/j.cities.2016.10.015Salas, J., & Yepes, V. (2019). MS-ReRO and D-ROSE methods: Assessing relational uncertainty and evaluating scenarios’ risks and opportunities on multi-scale infrastructure systems. Journal of Cleaner Production, 216, 607-623. doi:10.1016/j.jclepro.2018.12.083Dor, A., & Kissinger, M. (2017). A multi-year, multi-scale analysis of urban sustainability. Environmental Impact Assessment Review, 62, 115-121. doi:10.1016/j.eiar.2016.05.004Rega, C., Singer, J. P., & Geneletti, D. (2018). Investigating the substantive effectiveness of Strategic Environmental Assessment of urban planning: Evidence from Italy and Spain. Environmental Impact Assessment Review, 73, 60-69. doi:10.1016/j.eiar.2018.07.004Salas, J., & Yepes, V. (2018). A discursive, many-objective approach for selecting more-evolved urban vulnerability assessment models. Journal of Cleaner Production, 176, 1231-1244. doi:10.1016/j.jclepro.2017.11.249Penadés-Plà, V., García-Segura, T., Martí, J., & Yepes, V. (2016). A Review of Multi-Criteria Decision-Making Methods Applied to the Sustainable Bridge Design. Sustainability, 8(12), 1295. doi:10.3390/su8121295Zio, E., & Bazzo, R. (2011). A clustering procedure for reducing the number of representative solutions in the Pareto Front of multiobjective optimization problems. European Journal of Operational Research, 210(3), 624-634. doi:10.1016/j.ejor.2010.10.021Ishibuchi, H., Akedo, N., & Nojima, Y. (2015). Behavior of Multiobjective Evolutionary Algorithms on Many-Objective Knapsack Problems. IEEE Transactions on Evolutionary Computation, 19(2), 264-283. doi:10.1109/tevc.2014.2315442A fast and effective method for pruning of non-dominated solutions in many-objective problems https://www.scopus.com/inward/record.uri?eid=2-s2.0-33750253049&partnerID=40&md5=f46109796025a884fd054d73e71c308eTaboada, H. A., Baheranwala, F., Coit, D. W., & Wattanapongsakorn, N. (2007). Practical solutions for multi-objective optimization: An application to system reliability design problems. Reliability Engineering & System Safety, 92(3), 314-322. doi:10.1016/j.ress.2006.04.014Kasprzyk, J. R., Nataraj, S., Reed, P. M., & Lempert, R. J. (2013). Many objective robust decision making for complex environmental systems undergoing change. Environmental Modelling & Software, 42, 55-71. doi:10.1016/j.envsoft.2012.12.007Adger, W. N. (2006). Vulnerability. Global Environmental Change, 16(3), 268-281. doi:10.1016/j.gloenvcha.2006.02.006A new decision sciences for complex systems http://people.physics.anu.edu.au/~tas110/Teaching/Lectures/L1/Material/Lempert02.pdfThomas, J., & Kielman, J. (2009). Challenges for Visual Analytics. Information Visualization, 8(4), 309-314. doi:10.1057/ivs.2009.26Andrienko, G., Andrienko, N., Demsar, U., Dransch, D., Dykes, J., Fabrikant, S. I., … Tominski, C. (2010). Space, time and visual analytics. International Journal of Geographical Information Science, 24(10), 1577-1600. doi:10.1080/13658816.2010.508043Santos, J., Ferreira, A., & Flintsch, G. (2017). A multi-objective optimization-based pavement management decision-support system for enhancing pavement sustainability. Journal of Cleaner Production, 164, 1380-1393. doi:10.1016/j.jclepro.2017.07.027Análisis urbanístico de barrios vulnerables https://www.fomento.gob.es/MFOM/LANG_CASTELLANO/DIRECCIONES_GENERALES/ARQ_VIVIENDA/SUELO_Y_POLITICAS/OBSERVATORIO/Analisis_urba_Barrios_Vulnerables/Informes_CCAA.htmBirkmann, J., Garschagen, M., & Setiadi, N. (2014). New challenges for adaptive urban governance in highly dynamic environments: Revisiting planning systems and tools for adaptive and strategic planning. Urban Climate, 7, 115-133. doi:10.1016/j.uclim.2014.01.006Besagni, G., & Borgarello, M. (2019). The socio-demographic and geographical dimensions of fuel poverty in Italy. Energy Research & Social Science, 49, 192-203. doi:10.1016/j.erss.2018.11.007Khalil, N., Kamaruzzaman, S. N., & Baharum, M. R. (2016). Ranking the indicators of building performance and the users’ risk via Analytical Hierarchy Process (AHP): Case of Malaysia. Ecological Indicators, 71, 567-576. doi:10.1016/j.ecolind.2016.07.032Pellicer, E., Sierra, L. A., & Yepes, V. (2016). Appraisal of infrastructure sustainability by graduate students using an active-learning method. Journal of Cleaner Production, 113, 884-896. doi:10.1016/j.jclepro.2015.11.010Sierra, L. A., Yepes, V., & Pellicer, E. (2018). A review of multi-criteria assessment of the social sustainability of infrastructures. Journal of Cleaner Production, 187, 496-513. doi:10.1016/j.jclepro.2018.03.022Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9-26. doi:10.1016/0377-2217(90)90057-

Enhancing sustainability and resilience through multi-level infrastructure planning

[EN] Resilient planning demands not only resilient actions, but also resilient implementation, which promotes adaptive capacity for the attainment of the planned objectives. This requires, in the case of multi-level infrastructure systems, the simultaneous pursuit of bottom-up infrastructure planning for the promotion of adaptive capacity, and of top-down approaches for the achievement of global objectives and the reduction of structural vulnerabilities and imbalances. Though several authors have pointed out the need to balance bottom-up flexibility with top-down hierarchical control for better plan implementation, very few methods have yet been developed with this aim, least of all with a multi-objective perspective. This work addressed this lack by including, for the first time, the mitigation of urban vulnerability, the improvement of road network condition, and the minimization of the economic cost as objectives in a resilient planning process in which both actions and their implementation are planned for a controlled, sustainable development. Building on Urban planning support system (UPSS), a previously developed planning tool, the improved planning support system affords a planning alternative over the Spanish road network, with the best multi-objective balance between optimization, risk, and opportunity. The planning process then formalizes local adaptive capacity as the capacity to vary the selected planning alternative within certain limits, and global risk control as the duties that should be achieved in exchange. Finally, by means of multi-objective optimization, the method reveals the multi-objective trade-offs between local opportunity, global risk, and rights and duties at local scale, thus providing deeper understanding for better informed decision-making.This research was funded by the Spanish Ministry of Economy and Competitiveness, along with FEDER, grant number Project: BIA2017-85098-R.Salas, J.; Yepes, V. (2020). Enhancing sustainability and resilience through multi-level infrastructure planning. International Journal of Environmental research and Public Health. 17(3):1-22. https://doi.org/10.3390/ijerph17030962S122173Holling, C. S. (2004). From Complex Regions to Complex Worlds. Ecology and Society, 9(1). doi:10.5751/es-00612-090111Sharifi, A., & Yamagata, Y. (2014). Resilient Urban Planning: Major Principles and Criteria. Energy Procedia, 61, 1491-1495. doi:10.1016/j.egypro.2014.12.154Chen, Z., & Qiu, B. (2015). Resilient Planning Frame for Building Resilient Cities. GeoJournal Library, 33-41. doi:10.1007/978-3-319-14145-9_4Salas, J., & Yepes, V. (2019). MS-ReRO and D-ROSE methods: Assessing relational uncertainty and evaluating scenarios’ risks and opportunities on multi-scale infrastructure systems. Journal of Cleaner Production, 216, 607-623. doi:10.1016/j.jclepro.2018.12.083Schulz, A., Zia, A., & Koliba, C. (2015). Adapting bridge infrastructure to climate change: institutionalizing resilience in intergovernmental transportation planning processes in the Northeastern USA. Mitigation and Adaptation Strategies for Global Change, 22(1), 175-198. doi:10.1007/s11027-015-9672-xSharifi, A., & Yamagata, Y. (2018). Resilience-Oriented Urban Planning. Lecture Notes in Energy, 3-27. doi:10.1007/978-3-319-75798-8_1Gonzales, P., & Ajami, N. K. (2017). An integrative regional resilience framework for the changing urban water paradigm. Sustainable Cities and Society, 30, 128-138. doi:10.1016/j.scs.2017.01.012Leigh, N., & Lee, H. (2019). Sustainable and Resilient Urban Water Systems: The Role of Decentralization and Planning. Sustainability, 11(3), 918. doi:10.3390/su11030918Rogers, C. D. (2018). Engineering future liveable, resilient, sustainable cities using foresight. Proceedings of the Institution of Civil Engineers - Civil Engineering, 171(6), 3-9. doi:10.1680/jcien.17.00031Wagenaar, H., & Wilkinson, C. (2013). Enacting Resilience: A Performative Account of Governing for Urban Resilience. Urban Studies, 52(7), 1265-1284. doi:10.1177/0042098013505655Wei, Y. D., Li, H., & Yue, W. (2017). Urban land expansion and regional inequality in transitional China. Landscape and Urban Planning, 163, 17-31. doi:10.1016/j.landurbplan.2017.02.019France-Mensah, J., & O’Brien, W. J. (2019). Developing a Sustainable Pavement Management Plan: Tradeoffs in Road Condition, User Costs, and Greenhouse Gas Emissions. Journal of Management in Engineering, 35(3), 04019005. doi:10.1061/(asce)me.1943-5479.0000686Mao, X., Wang, J., Yuan, C., Yu, W., & Gan, J. (2018). A Dynamic Traffic Assignment Model for the Sustainability of Pavement Performance. Sustainability, 11(1), 170. doi:10.3390/su11010170Torres-Machi, C., Pellicer, E., Yepes, V., & Chamorro, A. (2017). Towards a sustainable optimization of pavement maintenance programs under budgetary restrictions. Journal of Cleaner Production, 148, 90-102. doi:10.1016/j.jclepro.2017.01.100Torres-Machi, C., Osorio, A., Godoy, P., Chamorro, A., Mourgues, C., & Videla, C. (2018). Sustainable Management Framework for Transportation Assets: Application to Urban Pavement Networks. KSCE Journal of Civil Engineering, 22(10), 4095-4106. doi:10.1007/s12205-018-1314-xOuma, Y. O., Opudo, J., & Nyambenya, S. (2015). Comparison of Fuzzy AHP and Fuzzy TOPSIS for Road Pavement Maintenance Prioritization: Methodological Exposition and Case Study. Advances in Civil Engineering, 2015, 1-17. doi:10.1155/2015/140189Viera Gomes, S., Cardoso, J. L., & Azevedo, C. L. (2018). Portuguese mainland road network safety performance indicator. Case Studies on Transport Policy, 6(3), 416-422. doi:10.1016/j.cstp.2017.10.006Heinitz, F. M. (2018). Consistency of state road network master plan development steps. Case Studies on Transport Policy, 6(3), 400-415. doi:10.1016/j.cstp.2017.08.001Rezaei, A., & Tahsili, S. (2018). Urban Vulnerability Assessment Using AHP. Advances in Civil Engineering, 2018, 1-20. doi:10.1155/2018/2018601Masi, A., Santarsiero, G., & Chiauzzi, L. (2014). Development of a seismic risk mitigation methodology for public buildings applied to the hospitals of Basilicata region (Southern Italy). Soil Dynamics and Earthquake Engineering, 65, 30-42. doi:10.1016/j.soildyn.2014.05.011Beilin, R., & Wilkinson, C. (2015). Introduction: Governing for urban resilience. Urban Studies, 52(7), 1205-1217. doi:10.1177/0042098015574955Cedergren, A., Johansson, J., & Hassel, H. (2018). Challenges to critical infrastructure resilience in an institutionally fragmented setting. Safety Science, 110, 51-58. doi:10.1016/j.ssci.2017.12.025Regmi, B. R., Star, C., & Leal Filho, W. (2014). Effectiveness of the Local Adaptation Plan of Action to support climate change adaptation in Nepal. Mitigation and Adaptation Strategies for Global Change, 21(3), 461-478. doi:10.1007/s11027-014-9610-3Frank, J., & Martinez-Vazquez, J. (Eds.). (2015). Decentralization and Infrastructure in the Global Economy. doi:10.4324/9781315694108Frank, J., & Martinez-Vazquez, J. (Eds.). (2015). Decentralization and Infrastructure in the Global Economy. doi:10.4324/9781315694108Lehmann, P., Brenck, M., Gebhardt, O., Schaller, S., & Süßbauer, E. (2013). Barriers and opportunities for urban adaptation planning: analytical framework and evidence from cities in Latin America and Germany. Mitigation and Adaptation Strategies for Global Change, 20(1), 75-97. doi:10.1007/s11027-013-9480-0Jain, M., & Korzhenevych, A. (2017). Spatial Disparities, Transport Infrastructure, and Decentralization Policy in the Delhi Region. Journal of Urban Planning and Development, 143(3), 05017003. doi:10.1061/(asce)up.1943-5444.0000379De Gregorio Hurtado, S. (2017). Is EU urban policy transforming urban regeneration in Spain? Answers from an analysis of the Iniciativa Urbana (2007–2013). Cities, 60, 402-414. doi:10.1016/j.cities.2016.10.015Newman, J. P., Dandy, G. C., & Maier, H. R. (2014). Multiobjective optimization of cluster-scale urban water systems investigating alternative water sources and level of decentralization. Water Resources Research, 50(10), 7915-7938. doi:10.1002/2013wr015233Gänzle, S., Stead, D., Sielker, F., & Chilla, T. (2018). Macro-regional Strategies, Cohesion Policy and Regional Cooperation in the European Union: Towards a Research Agenda. Political Studies Review, 17(2), 161-174. doi:10.1177/1478929918781982Roozbahani, A., Zahraie, B., & Tabesh, M. (2012). Integrated risk assessment of urban water supply systems from source to tap. Stochastic Environmental Research and Risk Assessment, 27(4), 923-944. doi:10.1007/s00477-012-0614-9Gupta, J., Bergsma, E., Termeer, C. J. A. M., Biesbroek, G. R., van den Brink, M., Jong, P., … Nooteboom, S. (2015). The adaptive capacity of institutions in the spatial planning, water, agriculture and nature sectors in the Netherlands. Mitigation and Adaptation Strategies for Global Change, 21(6), 883-903. doi:10.1007/s11027-014-9630-zRigillo, M., & Cervelli, E. (2014). Mapping Urban Vulnerability: The Case Study of Gran Santo Domingo, Dominican Republic. Advanced Engineering Forum, 11, 142-148. doi:10.4028/www.scientific.net/aef.11.142Salas, J., & Yepes, V. (2018). Urban vulnerability assessment: Advances from the strategic planning outlook. Journal of Cleaner Production, 179, 544-558. doi:10.1016/j.jclepro.2018.01.088Salas, J., & Yepes, V. (2018). A discursive, many-objective approach for selecting more-evolved urban vulnerability assessment models. Journal of Cleaner Production, 176, 1231-1244. doi:10.1016/j.jclepro.2017.11.249Zhao, P., Chapman, R., Randal, E., & Howden-Chapman, P. (2013). Understanding Resilient Urban Futures: A Systemic Modelling Approach. Sustainability, 5(7), 3202-3223. doi:10.3390/su5073202Salas, J., & Yepes, V. (2019). VisualUVAM: A Decision Support System Addressing the Curse of Dimensionality for the Multi-Scale Assessment of Urban Vulnerability in Spain. Sustainability, 11(8), 2191. doi:10.3390/su11082191Saku Kukkonen, & Jouni Lampinen. (2007). Ranking-Dominance and Many-Objective Optimization. 2007 IEEE Congress on Evolutionary Computation. doi:10.1109/cec.2007.4424990Navarro, I. J., Martí, J. V., & Yepes, V. (2019). Reliability-based maintenance optimization of corrosion preventive designs under a life cycle perspective. Environmental Impact Assessment Review, 74, 23-34. doi:10.1016/j.eiar.2018.10.001Adger, W. N. (2006). Vulnerability. Global Environmental Change, 16(3), 268-281. doi:10.1016/j.gloenvcha.2006.02.006Santos, J., Ferreira, A., & Flintsch, G. (2017). A multi-objective optimization-based pavement management decision-support system for enhancing pavement sustainability. Journal of Cleaner Production, 164, 1380-1393. doi:10.1016/j.jclepro.2017.07.027Zhang, Chen, Cai, Gao, Zhang, Liu, … Li. (2019). Analysis of the Spatial Distribution Characteristics of Urban Resilience and Its Influencing Factors: A Case Study of 56 Cities in China. International Journal of Environmental Research and Public Health, 16(22), 4442. doi:10.3390/ijerph16224442Baudrit, C., Taillandier, F., Tran, T. T. P., & Breysse, D. (2018). Uncertainty Processing and Risk Monitoring in Construction Projects Using Hierarchical Probabilistic Relational Models. Computer-Aided Civil and Infrastructure Engineering, 34(2), 97-115. doi:10.1111/mice.12391Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9-26. doi:10.1016/0377-2217(90)90057-iSingh, R. P., & Nachtnebel, H. P. (2016). Analytical hierarchy process (AHP) application for reinforcement of hydropower strategy in Nepal. Renewable and Sustainable Energy Reviews, 55, 43-58. doi:10.1016/j.rser.2015.10.138Convertino, M., Muñoz-Carpena, R., Chu-Agor, M. L., Kiker, G. A., & Linkov, I. (2014). Untangling drivers of species distributions: Global sensitivity and uncertainty analyses of MaxEnt. Environmental Modelling & Software, 51, 296-309. doi:10.1016/j.envsoft.2013.10.001Groen, E. A., Bokkers, E. A. M., Heijungs, R., & de Boer, I. J. M. (2016). Methods for global sensitivity analysis in life cycle assessment. The International Journal of Life Cycle Assessment, 22(7), 1125-1137. doi:10.1007/s11367-016-1217-3Evelyne Groen, Global Sensitivity Analysishttps://evelynegroen.github.io/Code/globalsensitivity.htmlConvertino, M., & Valverde, L. J. (2013). Portfolio Decision Analysis Framework for Value-Focused Ecosystem Management. PLoS ONE, 8(6), e65056. doi:10.1371/journal.pone.0065056García-Segura, T., Penadés-Plà, V., & Yepes, V. (2018). Sustainable bridge design by metamodel-assisted multi-objective optimization and decision-making under uncertainty. Journal of Cleaner Production, 202, 904-915. doi:10.1016/j.jclepro.2018.08.177McGlashan, A., Verrinder, G., & Verhagen, E. (2018). Working towards More Effective Implementation, Dissemination and Scale-Up of Lower-Limb Injury-Prevention Programs: Insights from Community Australian Football Coaches. International Journal of Environmental Research and Public Health, 15(2), 351. doi:10.3390/ijerph15020351YEPES, V., TORRES-MACHI, C., CHAMORRO, A., & PELLICER, E. (2016). OPTIMAL PAVEMENT MAINTENANCE PROGRAMS BASED ON A HYBRID GREEDY RANDOMIZED ADAPTIVE SEARCH PROCEDURE ALGORITHM. JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, 22(4), 540-550. doi:10.3846/13923730.2015.1120770Sierra, L. A., Yepes, V., & Pellicer, E. (2018). A review of multi-criteria assessment of the social sustainability of infrastructures. Journal of Cleaner Production, 187, 496-513. doi:10.1016/j.jclepro.2018.03.022Sierra, L. A., Pellicer, E., & Yepes, V. (2016). Social Sustainability in the Lifecycle of Chilean Public Infrastructure. Journal of Construction Engineering and Management, 142(5), 05015020. doi:10.1061/(asce)co.1943-7862.0001099Sierra, L. A., Yepes, V., García-Segura, T., & Pellicer, E. (2018). Bayesian network method for decision-making about the social sustainability of infrastructure projects. Journal of Cleaner Production, 176, 521-534. doi:10.1016/j.jclepro.2017.12.14

A discursive, many-objective approach for selecting more-evolved urban vulnerability assessment models

[EN] The development of more-evolved urban vulnerability assessment (UVA) models has become an increasingly important issue for both policy agendas and academia. Several requirements have already been set for this goal; they should be pursued simultaneously. However, methods with such integration are yet to be developed. The present paper addresses this integration via a discursive process in which interactions between decision makers and the method contribute to the selection of a model fulfilling these requirements. That model yields a UVA built upon both qualitative information and quantitative data from indicators selected for the neighbourhood, city, province, region and country political-administrative scales. The characteristics demanded are encoded both into the UVA assessment model and in the optimization and control modules governing the process. While the optimization produces compromise solutions, the control module supervises the process, provides dynamic control and enables the interactions. Interactions are informed with knowledge derived from the cognitive approach entailed by the method and afford a better understanding of the process dynamics. We conclude that the goodness of fit and time dynamics objectives are aligned. Therefore, UVA methods performing well for these objectives are available, although at the expense of a medium to poor performance in preferences and robustnessSalas, J.; Yepes, V. (2018). A discursive, many-objective approach for selecting more-evolved urban vulnerability assessment models. Journal of Cleaner Production. 176:1231-1244. https://doi.org/10.1016/j.jclepro.2017.11.249S1231124417

Absolute Expressiveness of Subgraph-Based Centrality Measures

In graph-based applications, a common task is to pinpoint the most important or "central" vertex in a (directed or undirected) graph, or rank the vertices of a graph according to their importance. To this end, a plethora of so-called centrality measures have been proposed in the literature. Such measures assess which vertices in a graph are the most important ones by analyzing the structure of the underlying graph. A family of centrality measures that are suited for graph databases has been recently proposed by relying on the following simple principle: the importance of a vertex in a graph is relative to the number of "relevant" connected subgraphs surrounding it; we refer to the members of this family as subgraph-based centrality measures. Although it has been shown that such measures enjoy several favourable properties, their absolute expressiveness remains largely unexplored. The goal of this work is to precisely characterize the absolute expressiveness of the family of subgraph-based centrality measures by considering both directed and undirected graphs. To this end, we characterize when an arbitrary centrality measure is a subgraph-based one, or a subgraph-based measure relative to the induced ranking. These characterizations provide us with technical tools that allow us to determine whether well-established centrality measures are subgraph-based. Such a classification, apart from being interesting in its own right, gives useful insights on the structural similarities and differences among existing centrality measures

The Water Utilities of Ciudad Juarez (JMAS), Chihuahua, Mexico: Water Management and the Modality of Decentralization

The last decades in Mexico have been marked by decentralization to the municipal level of water services. Questions arise relating to the best modality of municipal integration to the water administration systems and their services. Also questions come up involving the limitations and potential contributions of the municipal participation in the basin management. The present work has the objective to respond to these problems and to open a discussion about them. The study is based on a theoretical and empirical bibliographical revision on the participation of the local and state governments in water planning and management. The information stems from the updating of the Master Plan of Drinking Water and Sewer System of the JMAS, and from the national, state, and local press

Non-Linear Effects of Monetary Policy and Real Exchange Rate Shocks in Partially Dollarized Economies: An Empirical Study for Peru

We study whether monetary policy and real exchange rate shocks have non-linear effects on output and inflation in a partially dollarized economy such as Peru. For this purpose, we use a Smooth Transition Vector Autoregression methodology and then report impulse-response functions for shocks of different sign and size, and conditional to the initial position in the business cycle. We find evidence of non-linearities which imply a convex aggregate supply curve: in particular, monetary policy is more likely to affect the output during recessions than in booms, while the opposite is found for the inflation. Regarding real exchange rate shocks, we show that depreciations have greater negative effects during economic downturns and a higher pass-through rate in the positive side of the business cycle.Non-linearities, Monetary Policy, Smooth Transition VAR, Dollarization

The Equilibrium Real Exchange Rate in Peru: BEER Models and Confidence Band Building

This paper uses the "Behavioral Equilibrium Exchange Rate" (BEER) approach to estimate the equilibrium real exchange rate (RER) for Peru. A bootstrap technique is then employed to build confidence bands for the equilibrium path, so that it is possible to determine whether exchange rate misalignments are statistically significant. Additionally, structural breaks are modeled in the long-run relationship between the RER and its fundamentals. Using quarterly data for 1980.I-2005.III, the authors find that the long-run behavior of the Peruvian RER is explained by the following fundamentals: net foreign liabilities, terms of trade, and, less conclusively, government expenditure and openness. Moreover, the ratio of tradable to non-tradable sector productivities, both in domestic terms and relative to trading partners, appears as an additional RER fundamental only since the 1990s. Finally, there is evidence of some statistically significant RER misalignment episodes over the analyzed period.Equilibrium Real Exchange Rate, BEER Models, Cointegration, Structural Break, Bootstrap