Efectos de la política monetaria sobre el PIB

En los últimos años, el debate sobre la emisión monetaria ha tomado fuerza como fórmula para impulsar la reactivación económica. Sin embargo, existen posiciones diferentes en cuanto al tema. Para algunos la emisión sena sólo un problema inflacionario, lo que llevaría a perder todo lo que se ha ganado en este campo, sin tener ningún avance en materia de crecimiento económico y empleo. Otros por el contrario, piensan que una emisión monetaria por una sola vez y no de forma permanente podría generar una mayor recuperación en la economía. Usando la metodología VAR estructural, se buscó el efecto que sobre algunas variables económicas tuvo un shock de oferta monetaria. Así, se observa cómo una emisión no genera un crecimiento sostenido sobre la producción, sólo genera un aumento temporal de ella; por tanto, la política monetaria es una herramienta que puede contribuir a corregir las tendencias del ciclo económico, es decir, en situaciones de crisis, desaceleración o recalentamiento de la economía, la política monetaria puede ayudar a cambiar estas tendencias. De esta forma, quedaría por comprobar la hipótesis de cómo las mejores políticas para incentivar un crecimiento económico son aquellas que se enfocan en problemas estructurales y no coyunturales como, por ejemplo: factores tecnológicos y humanos, seguridad, etc.Throughout the past few years, the debate over the monetary emission has acquired vigor as the formula to impulse economical reactivation. Nonetheless, there are different positions to the matter. For some, the emission would only be an inflationary problem which would lead to the loss of all that has been gained in this field, without having any progress in matters of economical and employment growth. Others, contrarily, believe that a single monetary emission and not in a permanent manner, could genérate a greater recovery in the economy. Using the structural VAR methodology, it was sought after the effect which a shock of monetary offer had on some economical variables. Thus, it is observed how an emission does not generate a sustained growth over production, it only generates an temporal increase in it; therefore, the monetary policy is a tool that may contribute to correcting the tendencies of the economical cycle, that is, in crisis situations, deceleration or overheating of economy, the monetary policy may help change these tendencies. This way, it would only be left to prove the hypothesis that the best policies to induce an economical growth are those focused on structural and not circumstantial problems such as: technological and human factors, security, etc

Hierarchical clustering of spatially correlated functional data

Classification problems of functional data arise naturally in many applications. Several approaches have been considered for solving the problem of funding groups based on functional data. In this paper we are interested in detecting groups when the functional data are spatially correlated. Our methodology allows to find spatially homogeneous groups of sites when the observations at each sampling location consist of samples of random functions. In univariable and multivariable geostatistics various methods of incorporating spatial information into the clustering analysis have been considered. Here we extend these methods to the functional context in order to fulfill the task of clustering spatially correlated curves. In our approach we initially use basis functions to smooth the observed data and then we weight the dissimilarity matrix among curves by either the trace-variogram or the multivariable variogram calculated with the coeficients of the basis functions. As an illustration the methodology is applied to a real data set corresponding to average daily temperatures measured at 35 Canadian weather stations

Geofd: un paquete R para predicción geoestadística de datos funcionales

Spatially correlated curves are present in a wide range of applied disciplines. In this paper we describe the R package geofd which implements ordinary kriging prediction for this type of data. Initially the curves are pre-processed by fitting a Fourier or B-splines basis functions. After that the spatial dependence among curves is estimated by means of the tracevariogram function. Finally the parameters for performing prediction by ordinary kriging at unsampled locations are by estimated solving a linear system based estimated trace-variogram. We illustrate the software analyzing real and simulated data.Curvas espacialmente correlacionadas están presentes en un amplio rango de disciplinas aplicadas. En este trabajo se describe el paquete R geofd que implementa predicción por kriging ordinario para este tipo de datos. Inicialmente las curvas son suavizadas usando bases de funciones de Fourier o Bsplines. Posteriormente la dependencia espacial entre las curvas es estimada por la función traza-variograma. Finalmente los parámetros del predictor kriging ordinario son estimados resolviendo un sistema de ecuaciones basado en la estimación de la función traza-variograma. Se ilustra el paquete analizando datos reales y simulados.We would like to thank Andrés Pérez for his valuable contribution to upload the package geofd to CRAN. This work was partially supported by the Spanish Ministry of Education and Science through grants MTM2010-14961 and MTM2009- 13985-C02-01

An overview of kriging and cokriging predictors for functional random fields

This article presents an overview of methodologies for spatial prediction of functional data, focusing on both stationary and non-stationary conditions. A significant aspect of the functional random fields analysis is evaluating stationarity to characterize the stability of statistical properties across the spatial domain. The article explores methodologies from the literature, providing insights into the challenges and advancements in functional geostatistics. This work is relevant from theoreti cal and practical perspectives, offering an integrated view of methodologies tailored to the specific stationarity conditions of the functional processes under study. The practical implications of our work span across fields like environmental monitoring, geosciences, and biomedical research. This overview encourages advancements in functional geostatistics, paving the way for the development of innovative techniques for analyzing and predicting spatially correlated functional data. It lays the groundwork for future research, enhancing our understanding of spatial statistics and its applications.This research was partially supported by FONDECYT, grant number 1200525 (V.L.), from the National Agency for Research and Development (ANID) of the Chilean government under the Ministry of Science, Technology, Knowledge, and Innovation; and by Portuguese funds through the CMAT—Research Centre of Mathematics of University of Minho—within projects UIDB/00013/2020 and UIDP/00013/2020 (C.C.)

Análisis exploratorio de variables regionalizadas con métodos funcionales

Se muestra cómo las estadísticas descriptivas funcionales y el análisis en componentes principales funcional (ACPF) pueden emplearse en la evaluación empírica del supuesto de estacionariedad considerado en la modelación de variables regionalizadas. Se toma como ejemplo información georreferenciada correspondiente a mediciones de profundidad recogidas en 114 sitios de la Ciénaga Grande de Santa Marta, Colombia.It is shown how summary statistics of functional data and functional principal components analysis (FPCA) can be used to evaluate the stationarity assumption considered in modeling of regionalized variables. As an example is taken georeferenced information of depth measured at 114 locations at Ciénaga Grande de Santa Marta, Colombia

High leverage detection in general functional regression models with spatially correlated errors

The presence of curves that deviate markedly from the core of a set of curves can greatly affect inference and forecasting in a functional regression model. Thus their detection is key to increase the accuracy of the required estimates. This work introduces the concepts of high leverage in general functional regression models with independent and spatially correlated errors. The projectionmatrix, also known as Hat matrix, plays a crucial role in classicalmodel diagnosis, since it provides a measure of leverage. We propose a generalisation of the projection matrix in both the functional and the spatial functional frameworks under two settings, when the response variable is a scalar, and when it is a function itself, the so-called total model. Commonly used influence measures are also proposed as functions of the generalised functional leverages and residuals. An application of the proposed procedures for investigating the effect of outliers on the relationship between transformation of the banking industry and the size of cooperative banks in Italy over a period of 14 years is presented

Geostatistics for functional data: an ordinary kriging approach

We present a methodology to perform spatial prediction when measured data are curves. In particular, we propose both an estimator of the spatial correlation and a functional kriging predictor. We adapt an optimization criterium used in multi- variable spatial prediction in order to estimate the kriging parameters. A real data example on soil penetration resistences illustrates our proposals

Continuous time-varying kriging for spatial prediction of functional data: An environmental application

Spatially correlated functional data is present in a wide range of environmental disciplines and, in this context, efficient prediction of curves is a key issue. We present an approach for spatial prediction based on the functional linear point-wise model adapted to the case of spatially correlated curves. First, a smoothing process is applied to the curves by expanding the curves and the functional parameters in terms of a set of Fourier basis functions. The number of basis functions is chosen by cross-validation. Then, the spatial prediction of a curve is obtained as a point-wise linear combination of the smoothed data. The prediction problem is solved by estimating a linear model of coregionalization to set the spatial dependence among the fitted coefficients. We extend an optimization criterion used in multivariable geostatistics to the functional context. The method is illustrated by smoothing and predicting temperature curves measured at 35 Canadian weather stations

Propuesta de un indicador como variable auxiliar en el análisis cokriging

Se propone un indicador que facilita la estimación del modelo lineal de corregionalización (MLC), necesario para la aplicación de la técnica cokriging. A través de un estudio de caso se comparan varianzas de predicción obtenidas con las técnicas kriging y cokriging empleando como variable auxiliar el indicador propuesto. Se muestra, para la información considerada, que el método cokriging resulta preferible en términos de costos y de precisión de las predicciones.It is proposed an indicator that facilitates the estimation of the linear model of coregionalization necessary for the application of the cokriging method. Through a case study, the prediction variances, obtained by kriging and cokriging methods with the proposed indicator as auxiliary variable, are compared. It is shown, with the considered information, that the cokriging method is preferable in terms of costs and precision of the predictions

Efectos de la política monetaria sobre el PIB

Throughout the past few years, the debate over the monetary emission has acquired vigor as the formula to impulse economical reactivation. Nonetheless, there are different positions to the matter. For some, the emission would only be an inflationary problem which would lead to the loss of all that has been gained in this field, without having any progress in matters of economical and employment growth. Others, contrarily, believe that a single monetary emission and not in a permanent manner, could genérate a greater recovery in the economy. Using the structural VAR methodology, it was sought after the effect which a shock of monetary offer had on some economical variables. Thus, it is observed how an emission does not generate a sustained growth over production, it only generates an temporal increase in it; therefore, the monetary policy is a tool that may contribute to correcting the tendencies of the economical cycle, that is, in crisis situations, deceleration or overheating of economy, the monetary policy may help change these tendencies. This way, it would only be left to prove the hypothesis that the best policies to induce an economical growth are those focused on structural and not circumstantial problems such as: technological and human factors, security, etc.En los últimos años, el debate sobre la emisión monetaria ha tomado fuerza como fórmula para impulsar la reactivación económica. Sin embargo, existen posiciones diferentes en cuanto al tema. Para algunos la emisión sena sólo un problema inflacionario, lo que llevaría a perder todo lo que se ha ganado en este campo, sin tener ningún avance en materia de crecimiento económico y empleo. Otros por el contrario, piensan que una emisión monetaria por una sola vez y no de forma permanente podría generar una mayor recuperación en la economía. Usando la metodología VAR estructural, se buscó el efecto que sobre algunas variables económicas tuvo un shock de oferta monetaria. Así, se observa cómo una emisión no genera un crecimiento sostenido sobre la producción, sólo genera un aumento temporal de ella; por tanto, la política monetaria es una herramienta que puede contribuir a corregir las tendencias del ciclo económico, es decir, en situaciones de crisis, desaceleración o recalentamiento de la economía, la política monetaria puede ayudar a cambiar estas tendencias. De esta forma, quedaría por comprobar la hipótesis de cómo las mejores políticas para incentivar un crecimiento económico son aquellas que se enfocan en problemas estructurales y no coyunturales como, por ejemplo: factores tecnológicos y humanos, seguridad, etc