Real effects of inflation uncertainty in the US

We empirically investigate the effects of inflation uncertainty on output growth for the US using both monthly and quarterly data over 1985-2009. Employing a Markov regime switching approach to model output dynamics, we show that inflation uncertainty obtained from a Markov regime switching GARCH model exerts a negative and regime dependant impact on output growth. In particular, we show that the negative impact of inflation uncertainty on output growth is almost 4.5 times higher during the low growth regime than that during the high growth regime. We verify the robustness of our findings using quarterly data

The impact of inflation uncertainty on economic growth: a MRS-IV approach

We empirically investigate inflation uncertainty effects on output growth for the US by implementing a Markov regime switching model as we account for endogeneity problems. We show that inflation uncertainty -obtained from a Markov regime switching GARCH model - has a negative and regime dependent impact on output growth. Moreover, we find that the smooth probability of high growth regime falls long before the recent financial crisis was imminent. This might be driven by a regime dependent causality, an issue which has been left unexplored

Evaluating currency crises: A multivariate Markov regime switching approach

This paper provides an empirical framework to analyse the nature of currency crises byextending earlier work of Jeanne and Masson (2000) who suggest that a currency crisismodel with multiple equilibria can be estimated using Markov regime switching (MRS)models. However, Jeanne and Masson (2000) assume that the transition probabilitiesacross equilibria are constant and independent of fundamentals. Thus, currency crisis isdriven by a sunspot unrelated to fundamentals. This paper further contributes to theliterature by suggesting a multivariate MRS model to analyse the nature of currencycrises. In the new set up, one can test for the impact of the unobserved dynamics offundamentals on the probability of devaluation. Empirical evidence shows thatexpectations about fundamentals, which are reflected by their unobserved state variables,not only affect the probability of devaluation but also can be used to forecast a currencycrisis one period ahead

Effects of Inflation on Output Gap: A MRS-IV Approach

In this paper, we propose an analytical framework to explore the level and volatility effects of inflation on the output gap. Using quarterly US data over 1977:q2-2009:q4, we then examine the empirical implications of the model by implementing an instrumental variables Markov regime switching approach. We show that inflation uncertainty has a negative and regime dependent impact on the output gap but the level of inflation does not have any such effect. Our empirical investigation also provides evidence that the US economy is moving towards a period of turmoil before the recent financial crisis was imminent. The results are robust to the use of alternative measures of inflation uncertainty

Monetary Policy Preferences of the EMU and the UK

We estimate the central bank policy preferences for the European Monetary Union and for the UK. In doing so, we extend the theoretical framework suggested by Cecchetti et al. (2002) by assuming that policy preferences change across different regimes either due to the different phases of the business cycle, or due to changes in propagation mechanism, or due to volatility shifts of the underlying structural shocks. Our empirical results suggest that the weight that policy makers put on inflation is typically profound. Furthermore, it appears that volatility shifts of the economic disturbances is the main factor, which generates variation in policy preferences.This is the author accepted manuscript. The final version is available from Wiley via https://doi.org/10.1111/manc.1212

Optimal matching between spatial datasets under capacity constraints

Consider a set of customers (e.g., WiFi receivers) and a set of service providers (e.g., wireless access points), where each provider has a capacity and the quality of service offered to its customers is anti-proportional to their distance. The Capacity Constrained Assignment (CCA) is a matching between the two sets such that (i) each customer is assigned to at most one provider, (ii) every provider serves no more customers than its capacity, (iii) the maximum possible number of customers are served, and (iv) the sum of Euclidean distances within the assigned provider-customer pairs is minimized. Although max-flow algorithms are applicable to this problem, they require the complete distance-based bipartite graph between the customer and provider sets. For large spatial datasets, this graph is expensive to compute and it may be too large to fit in main memory. Motivated by this fact, we propose efficient algorithms for optimal assignment that employ novel edge-pruning strategies, based on the spatial properties of the problem. Additionally, we develop incremental techniques that maintain an optimal assignment (in the presence of updates) with a processing cost several times lower than CCA recomputation from scratch. Finally, we present approximate (i.e., suboptimal) CCA solutions that provide a tunable trade-off between result accuracy and computation cost, abiding by theoretical quality guarantees. A thorough experimental evaluation demonstrates the efficiency and practicality of the proposed techniques. © 2010 ACM.postprin

A framework for privacy and security requirements analysis and conflict resolution for supporting GDPR compliance through privacy-by-design

Requirements elicitation, analysis, and, above all, early detection of conflicts and resolution, are among the most important, strategic, complex and crucial activities for preventing software system failures, and reducing costs related to reengineering/fixing actions. This is especially important when critical Requirements Classes are involved, such as Privacy and Security Requirements. Recently, organisations have been heavily fined for lack of compliance with data protection regulations, such as the EU General Data Protection Regulation (GDPR). GDPR requires organisations to enforce privacy-by-design activities from the early stages and for the entire software engineering cycle. Accordingly, requirements engineers need methods and tools for systematically identifying privacy and security requirements, detecting and solving related conflicts. Existing techniques support requirements identification without detecting or mitigating conflicts. The framework and tool we propose in this paper, called ConfIs, fills this gap by supporting engineers and organisations in these complex activities, with its systematic and interactive process. We applied ConfIs to a realistic GDPR example from the DEFeND EU Project, and evaluated its supportiveness, with positive results, by involving privacy and security requirements experts (This research is an extension of the study conducted by Alkubaisy et al. [1] – which itself is a continuation of earlier studies [2, 3] and aims to aid the reader in comprehensively grasping the concepts laid out)

Sustainable transport modes, travel satisfaction, and emotions: Evidence from car-dependent compact cities

This study investigates how the use of sustainable transport modes relates to travel satisfaction (general evaluation of travel) and travel affect (emotions during travel) in car-dependent compact cities. Thereby, the study provides evidence on sustainable mobility and travel-related well-being in a context of compact urban form but inadequate provisions for public transport, walking, and cycling. A mixed-methods approach was applied comprising quantitative and qualitative analyses of data from the two major cities of Greece, i.e., Athens and Thessaloniki. Travel satisfaction and travel affect are found to be highest for those who walk for commuting, independently of travel time and other factors. Conversely, travel satisfaction and travel affect are lowest for public transport users, largely due to very long travel times but also poor public transport services in one of the two cities. Results indicate that the experience of traveling by public transport, car, and motorcycle within urban areas greatly depends on transport provision and policies. Overall, findings support the idea that to shift to pleasant, satisfying, and sustainable mobility in car-dependent compact cities, car restrictions should be accompanied by massive improvements in public transport, high-quality walking and cycling infrastructure, and an integrated coordination of different modes

Confis: a tool for privacy and security analysis and conflict resolution for supporting GDPR compliance through privacy-by-design

Privacy and security requirements, and their potential conflicts, are increasingly having more and more importance. It is becoming a necessary part to be considered, starting from the very early stages of requirements engineering, and in the entire software engineering cycle, for the design of any software system. In the last few years, this has been even more emphasized and required by the law. A relevant example is the case of the General Data Protection Regulation (GDPR), which requires organizations, and their software engineers, to enforce and guarantee privacy-by-design to make their platforms compliant with the regulation. In this context, complex activities related to privacy and security requirements elicitation, analysis, mapping and identification of potential conflicts, and the individuation of their resolution, become crucial. In the literature, there is not available a comprehensive requirement engineering oriented tool for supporting the requirements analyst. In this p aper, we propose ConfIs, a tool for supporting the analyst in performing a process covering these phases in a systematic and interactive way. We present ConfIs and its process with a realistic example from DEFeND, an EU project aiming at supporting organizations in achieving GDPR compliance. In this context, we evaluated ConfIs by involving privacy/security requirements experts, which recognized our tool and method as supportive, concerning these complex activities

Does a residential relocation enable satisfying travel?

Transport-related residential self-selection indicates that people try to live in a neighbourhood in line with their travel preferences and needs. Although studies have found that travel attitudes are mostly aligned with urban form characteristics of the residential location, no studies have explored whether people are actually able to travel in their preferred way after having relocated. In this study we analyse whether individuals’ travel patterns are consistent with their travel preferences following residential relocation and if this congruency affects their travel satisfaction. Results from 1650 recently relocated residents in the city of Ghent (Belgium) indicate that most respondents were able to change their travel behaviour in congruence with their travel attitudes. The study found that a decrease in travel duration, distance, car use, and public transport use, and an increase in walking and cycling increased travel satisfaction. This is particularly true when changes in travel behaviour interacted with travel attitudes. Results show that when walking and cycling levels change in line with travel attitudes, travel satisfaction increases strongly. However, the interaction between travel behaviour changes and travel attitudes does not always explain travel satisfaction (improvements). We found, for instance, that individuals with reduced travel durations, despite having a positive attitude towards travel in general, have high levels of travel satisfaction (improvements). The findings indicate that built environment interventions enabling a transport-related self-selection process have the potential to contribute to satisfying travel and thereby to improve subjective well-being of residents