Effectiveness of the monetary policy framework in present-day India : have financial variables functioned as useful policy indicators

In April 1998, the RBI, the Indian central bank, formally announced a shift in its policy framework from monetary targeting to a multiple indicator approach, and since then, under this framework, the bank has considered a range of economic and financial variables as policy indicators for drawing policy perspectives. This paper aims to examine the effectiveness of this current policy framework in India by analyzing the causal relationships of each indicator variable on the objective variables. The results reveal that, except for bank credit, all indicator variables considered in this study have a causal relationship with at least either output or price level, suggesting that most preannounced economic and financial variables have served as useful policy indicators under the multiple indicator approach.

El Potencial de las Variables Latentes en Modelos Explicativos del Uso de la Bicicleta

La necesidad de una movilidad más sostenible en nuestras ciudades hacen que la bicicleta tome cada vez mayor importancia como modo de transporte urbano. A su vez, también aumenta el interés de los investigadores por conocer mejor los factores relacionados con el uso de la bicicleta. Tradicionalmente se ha prestado atención a los factores relacionados con el uso de la bicicleta que son directamente observables, dejando en un segundo plano las percepciones, actitudes o normas sociales de los usuarios respecto a este modo de transporte. Esta tesis ha profundizado en este segundo tipo de factores mediante la búsqueda de variables latentes que puedan definirlos. Se parte contemplando las características de la bicicleta como modo urbano de transporte y los condicionantes territoriales para su uso. En los siguientes capítulos se hace un repaso exhaustivo de los factores que condicionan el uso de la bicicleta en la literatura científica. También se profundiza en el planteamiento de las teorías del comportamiento del usuario de transporte para poder desarrollar el marco teórico sobre el que enfocar la búsqueda de las variables latentes. Finalmente, se repasa el tratamiento recibido por la bicicleta en las técnicas de análisis de la demanda. La hipótesis de partida mantenida plantea que las variables psicosociales pueden ser especialmente influyentes en la decisión de usar la bicicleta. Estas variables, junto con las variables tradicionales de los usuarios de transporte, mejorarían el conocimiento que tenemos sobre los factores que inciden en su uso. Con la finalidad de poder explorar e identificar estas variables se propone una metodología basada en modelos de ecuaciones estructurales. Estos modelos permiten contemplar las interacciones entre variables y trabajar con variables latentes que medimos a través de indicadores. Aplicando la metodología propuesta a un caso de estudio en la Ciudad Universitaria de Madrid se ha podido identificar cuatro variables latentes: conveniencia, probici, limitaciones externas y condicionantes físicos. Estas variables mejoran el conocimiento sobre los factores explicativos de la decisión de usar la bicicleta entre los usuarios de Ciudad Universitaria.

Identifying Moderator Variables Through Requirements Elicitation Experiments Limitations

Interviews are the most widely used elicitation technique in Requirements Engineering (RE). Despite its importance, research in interviews is quite limited, in particular from an experimental perspective. We have performed a series of experiments exploring the relative effectiveness of structured and unstructured interviews. This line of research has been active in Information Systems in the past years, so that our experiments can be aggregated together with existing ones to obtain guidelines for practice. Experimental aggregation is a demanding task. It requires not only a large number of experiments, but also considering the influence of the existing moderators. However, in the current state of the practice in RE, those moderators are unknown. We believe that analyzing the threats to validity in interviewing experiments may give insight about how to improve further replications and the corresponding aggregations. It is likely that this strategy may be applied in other Software Engineering areas as well.

Trends in climatic variables and future reference evapotranspiration in Duero Valley (Spain)

The impact of climate change and its relation with evapotranspiration was evaluated in the Duero River Basin (Spain). The study shows possible future situations 50 yr from now from the reference evapotranspiration (ETo). The maximum temperature (Tmax), minimum temperature (Tmin), dew point (Td), wind speed (U) and net radiation (Rn) trends during the 1980–2009 period were obtained and extrapolated with the FAO-56 Penman-Montheith equation to estimate ETo. Changes in stomatal resistance in response to increases in CO2 were also considered. Four scenarios were done, taking the concentration of CO2 and the period analyzed (annual or monthly) into consideration. The scenarios studied showed the changes in ETo as a consequence of the annual and monthly trends in the variables Tmax, Tmin, Td, U and Rn with current and future CO2 concentrations (372 ppm and 550 ppm). The future ETo showed increases between 118 mm (11 %) and 55 mm (5 %) with respect to the current situation of the river basin at 1042 mm. The months most affected by climate change are May, June, July, August and September, which also coincide with the maximum water needs of the basin’s crops

An Interval-based Multiobjective Approach to Feature Subset Selection Using Joint Modeling of Objectives and Variables

This paper studies feature subset selection in classification using a multiobjective estimation of distribution algorithm. We consider six functions, namely area under ROC curve, sensitivity, specificity, precision, F1 measure and Brier score, for evaluation of feature subsets and as the objectives of the problem. One of the characteristics of these objective functions is the existence of noise in their values that should be appropriately handled during optimization. Our proposed algorithm consists of two major techniques which are specially designed for the feature subset selection problem. The first one is a solution ranking method based on interval values to handle the noise in the objectives of this problem. The second one is a model estimation method for learning a joint probabilistic model of objectives and variables which is used to generate new solutions and advance through the search space. To simplify model estimation, l1 regularized regression is used to select a subset of problem variables before model learning. The proposed algorithm is compared with a well-known ranking method for interval-valued objectives and a standard multiobjective genetic algorithm. Particularly, the effects of the two new techniques are experimentally investigated. The experimental results show that the proposed algorithm is able to obtain comparable or better performance on the tested datasets.