Trimestralización y conciliación de magnitudes económicas : una ampliación del método Chow-Lin

Una de las herramientas estadísticas más importantes para el seguimiento y análisis de la evolución de la actividad económica a corto plazo es la disponibilidad de estimaciones de la evolución trimestral de los componentes del PIB, en lo que afecta tanto a la oferta como a la demanda. La necesidad de disponer de esta información con un retraso temporal reducido hace imprescindible la utilización de métodos de trimestralización que permitan desagregar la información anual a trimestral. El método más aplicado, puesto que permite resolver este problema de manera muy elegante bajo un enfoque estadístico de estimador óptimo, es el método de Chow-Lin. Pero este método no garantiza que las estimaciones trimestrales del PIB en lo que respecta a la oferta y a la demanda coincidan, haciendo necesaria la aplicación posterior de algún método de conciliación. En este trabajo se desarrolla una ampliación multivariante del método de Chow-Lin que permite resolver el problema de la estimación de los valores trimestrales de manera óptima, sujeta a un conjunto de restricciones. Una de las aplicaciones potenciales de este método, que hemos denominado método de Chow-Lin restringido, es precisamente la estimación conjunta de valores trimestrales para cada uno de los componentes del PIB en lo que afecta tanto a la demanda como a la oferta condicionada a que ambas estimaciones trimestrales del PIB sean iguales, evitando así la necesidad de aplicar posteriormente métodos de conciliación

Transformation kernel density estimation of actuarial loss functions

[cat] Es presenta un estimador nucli transformat que és adequat per a distribucions de cua pesada. Utilitzant una transformació basada en la distribució de probabilitat Beta l’elecció del paràmetre de finestra és molt directa. Es presenta una aplicació a dades d’assegurances i es mostra com calcular el Valor en Risc.

Una extensión de la regresión propuesta por Geweke y Porter-Hudak para la estimación del orden de diferenciación en modelos ARFIMA

Recientemente, ha aumentado mucho el interés por la aplicación de los modelos de memoria larga a variables económicas, sobre todo los modelos ARFIMA. Sin duda , el método más usado para la estimación de estos modelos en el ámbito del análisis económico es el propuesto por Geweke y Portero-Hudak (GPH) aun cuando en trabajos recientes se ha demostrado que, en ciertos casos, este estimador presenta un sesgo muy importante. De ahí que, se propone una extensión de este estimador a partir del modelo exponencial propuesto por Bloomfield, y que permite corregir este sesgo.A continuación, se analiza y compara el comportamiento de ambos estimadores en muestras no muy grandes y se comprueba como el estimador propuesto presenta un error cuadrático medio menor que el estimador GPH

Identifying the socioeconomic determinants of crime in Spanish provinces

In this paper we study, having as theoretical reference the economic model of crime (Becker, 1968; Ehrlich, 1973), which are the socioeconomic and demographic determinants of crime in Spain paying attention on the role of provincial peculiarities. We estimate a crime equation using a panel dataset of Spanish provinces (NUTS3) for the period 1993 to 1999 employing the GMMsystem estimator. Empirical results suggest that lagged crime rate and clear-up rate are correlated to all typologies of crime rate considered. Property crimes are better explained by socioeconomic variables (GDP per capita, GDP growth rate and percentage of population with high school and university degree), while demographic factors reveal important and significant influences, in particular for crimes against the person. These results are obtained using an instrumental variable approach that takes advantage of the dynamic properties of our dataset to control for both measurement errors in crime data and joint endogeneity of the explanatory variables

Una extensión de la regresión propuesta por Geweke y Porter-Hudak para la estimación del orden de diferenciación en modelos ARFIMA

Recientemente, ha aumentado mucho el interés por la aplicación de los modelos de memoria larga a variables económicas, sobre todo los modelos ARFIMA. Sin duda , el método más usado para la estimación de estos modelos en el ámbito del análisis económico es el propuesto por Geweke y Portero-Hudak (GPH) aun cuando en trabajos recientes se ha demostrado que, en ciertos casos, este estimador presenta un sesgo muy importante. De ahí que, se propone una extensión de este estimador a partir del modelo exponencial propuesto por Bloomfield, y que permite corregir este sesgo.A continuación, se analiza y compara el comportamiento de ambos estimadores en muestras no muy grandes y se comprueba como el estimador propuesto presenta un error cuadrático medio menor que el estimador GPH

Transformation kernel density estimation of actuarial loss functions

[cat] Es presenta un estimador nucli transformat que és adequat per a distribucions de cua pesada. Utilitzant una transformació basada en la distribució de probabilitat Beta l’elecció del paràmetre de finestra és molt directa. Es presenta una aplicació a dades d’assegurances i es mostra com calcular el Valor en Risc.

Linear least squares estimation of the first order moving average parameter

We propose an iterative procedure to minimize the sum of squares function which avoids the nonlinear nature of estimating the first order moving average parameter and provides a closed form of the estimator. The asymptotic properties of the method are discussed and the consistency of the linear least squares estimator is proved for the invertible case. We perform various Monte Carlo experiments in order to compare the sample properties of the linear least squares estimator with its nonlinear counterpart for the conditional and unconditional cases. Some examples are also discussed

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

Trimestralización y conciliación de magnitudes económicas : una ampliación del método Chow-Lin

Una de las herramientas estadísticas más importantes para el seguimiento y análisis de la evolución de la actividad económica a corto plazo es la disponibilidad de estimaciones de la evolución trimestral de los componentes del PIB, en lo que afecta tanto a la oferta como a la demanda. La necesidad de disponer de esta información con un retraso temporal reducido hace imprescindible la utilización de métodos de trimestralización que permitan desagregar la información anual a trimestral. El método más aplicado, puesto que permite resolver este problema de manera muy elegante bajo un enfoque estadístico de estimador óptimo, es el método de Chow-Lin. Pero este método no garantiza que las estimaciones trimestrales del PIB en lo que respecta a la oferta y a la demanda coincidan, haciendo necesaria la aplicación posterior de algún método de conciliación. En este trabajo se desarrolla una ampliación multivariante del método de Chow-Lin que permite resolver el problema de la estimación de los valores trimestrales de manera óptima, sujeta a un conjunto de restricciones. Una de las aplicaciones potenciales de este método, que hemos denominado método de Chow-Lin restringido, es precisamente la estimación conjunta de valores trimestrales para cada uno de los componentes del PIB en lo que afecta tanto a la demanda como a la oferta condicionada a que ambas estimaciones trimestrales del PIB sean iguales, evitando así la necesidad de aplicar posteriormente métodos de conciliación

Identifying the socioeconomic determinants of crime in Spanish provinces

In this paper we study, having as theoretical reference the economic model of crime (Becker, 1968; Ehrlich, 1973), which are the socioeconomic and demographic determinants of crime in Spain paying attention on the role of provincial peculiarities. We estimate a crime equation using a panel dataset of Spanish provinces (NUTS3) for the period 1993 to 1999 employing the GMMsystem estimator. Empirical results suggest that lagged crime rate and clear-up rate are correlated to all typologies of crime rate considered. Property crimes are better explained by socioeconomic variables (GDP per capita, GDP growth rate and percentage of population with high school and university degree), while demographic factors reveal important and significant influences, in particular for crimes against the person. These results are obtained using an instrumental variable approach that takes advantage of the dynamic properties of our dataset to control for both measurement errors in crime data and joint endogeneity of the explanatory variables

Evolution of source EEG synchronization in early Alzheimer's disease.

Alzheimer's disease (AD) disrupts functional connectivity in distributed cortical networks. We analyzed changes in the S-estimator, a measure of multivariate intraregional synchronization, in electroencephalogram (EEG) source space in 15 mild AD patients versus 15 age-matched controls to evaluate its potential as a marker of AD progression. All participants underwent 2 clinical evaluations and 2 EEG recording sessions on diagnosis and after a year. The main effect of AD was hyposynchronization in the medial temporal and frontal regions and relative hypersynchronization in posterior cingulate, precuneus, cuneus, and parietotemporal cortices. However, the S-estimator did not change over time in either group. This result motivated an analysis of rapidly progressing AD versus slow-progressing patients. Rapidly progressing AD patients showed a significant reduction in synchronization with time, manifest in left frontotemporal cortex. Thus, the evolution of source EEG synchronization over time is correlated with the rate of disease progression and should be considered as a cost-effective AD biomarker.

Estimation of jump-diffusion processes via empirical characteristic functions

Preface The starting point for this work and eventually the subject of the whole thesis was the question: how to estimate parameters of the affine stochastic volatility jump-diffusion models. These models are very important for contingent claim pricing. Their major advantage, availability T of analytical solutions for characteristic functions, made them the models of choice for many theoretical constructions and practical applications. At the same time, estimation of parameters of stochastic volatility jump-diffusion models is not a straightforward task. The problem is coming from the variance process, which is non-observable. There are several estimation methodologies that deal with estimation problems of latent variables. One appeared to be particularly interesting. It proposes the estimator that in contrast to the other methods requires neither discretization nor simulation of the process: the Continuous Empirical Characteristic function estimator (EGF) based on the unconditional characteristic function. However, the procedure was derived only for the stochastic volatility models without jumps. Thus, it has become the subject of my research. This thesis consists of three parts. Each one is written as independent and self contained article. At the same time, questions that are answered by the second and third parts of this Work arise naturally from the issues investigated and results obtained in the first one. The first chapter is the theoretical foundation of the thesis. It proposes an estimation procedure for the stochastic volatility models with jumps both in the asset price and variance processes. The estimation procedure is based on the joint unconditional characteristic function for the stochastic process. The major analytical result of this part as well as of the whole thesis is the closed form expression for the joint unconditional characteristic function for the stochastic volatility jump-diffusion models. The empirical part of the chapter suggests that besides a stochastic volatility, jumps both in the mean and the volatility equation are relevant for modelling returns of the S&P500 index, which has been chosen as a general representative of the stock asset class. Hence, the next question is: what jump process to use to model returns of the S&P500. The decision about the jump process in the framework of the affine jump- diffusion models boils down to defining the intensity of the compound Poisson process, a constant or some function of state variables, and to choosing the distribution of the jump size. While the jump in the variance process is usually assumed to be exponential, there are at least three distributions of the jump size which are currently used for the asset log-prices: normal, exponential and double exponential. The second part of this thesis shows that normal jumps in the asset log-returns should be used if we are to model S&P500 index by a stochastic volatility jump-diffusion model. This is a surprising result. Exponential distribution has fatter tails and for this reason either exponential or double exponential jump size was expected to provide the best it of the stochastic volatility jump-diffusion models to the data. The idea of testing the efficiency of the Continuous ECF estimator on the simulated data has already appeared when the first estimation results of the first chapter were obtained. In the absence of a benchmark or any ground for comparison it is unreasonable to be sure that our parameter estimates and the true parameters of the models coincide. The conclusion of the second chapter provides one more reason to do that kind of test. Thus, the third part of this thesis concentrates on the estimation of parameters of stochastic volatility jump- diffusion models on the basis of the asset price time-series simulated from various "true" parameter sets. The goal is to show that the Continuous ECF estimator based on the joint unconditional characteristic function is capable of finding the true parameters. And, the third chapter proves that our estimator indeed has the ability to do so. Once it is clear that the Continuous ECF estimator based on the unconditional characteristic function is working, the next question does not wait to appear. The question is whether the computation effort can be reduced without affecting the efficiency of the estimator, or whether the efficiency of the estimator can be improved without dramatically increasing the computational burden. The efficiency of the Continuous ECF estimator depends on the number of dimensions of the joint unconditional characteristic function which is used for its construction. Theoretically, the more dimensions there are, the more efficient is the estimation procedure. In practice, however, this relationship is not so straightforward due to the increasing computational difficulties. The second chapter, for example, in addition to the choice of the jump process, discusses the possibility of using the marginal, i.e. one-dimensional, unconditional characteristic function in the estimation instead of the joint, bi-dimensional, unconditional characteristic function. As result, the preference for one or the other depends on the model to be estimated. Thus, the computational effort can be reduced in some cases without affecting the efficiency of the estimator. The improvement of the estimator s efficiency by increasing its dimensionality faces more difficulties. The third chapter of this thesis, in addition to what was discussed above, compares the performance of the estimators with bi- and three-dimensional unconditional characteristic functions on the simulated data. It shows that the theoretical efficiency of the Continuous ECF estimator based on the three-dimensional unconditional characteristic function is not attainable in practice, at least for the moment, due to the limitations on the computer power and optimization toolboxes available to the general public. Thus, the Continuous ECF estimator based on the joint, bi-dimensional, unconditional characteristic function has all the reasons to exist and to be used for the estimation of parameters of the stochastic volatility jump-diffusion models.

Independent contribution of parental migrant status and educational level to adiposity and eating habits in preschool children.

Background/Objective:Little is known about the precise role of parental migrant status (MS) and educational level (EL) on adiposity and various eating habits in young children. Therefore, we assessed their independent contribution in preschoolers.Subjects/Methods:Of 655 randomly selected preschoolers, 542 (5.1±0.6 years; 71% of parental MS and 37% of low parental EL) were analysed. Body composition was measured by bioelectrical impedance. Eating habits were assessed using a semiqualitative food frequency questionnaire and analysed according to five messages developed by the Swiss Society for Nutrition, based on factors implicated in childhood obesity: (1) 'Drinking water and decreasing sweetened drinks', (2) 'Eating fruit and vegetables', (3) 'Decreasing breakfast skipping', (4) 'Reducing fatty and sweet foods' and (5) 'Reducing the intake of meals and snacks in front of television'.Results:Children of migrant and low EL parents had higher body fat, ate more meals and snacks while watching television and had more fruit and fatty foods compared with their respective counterparts (all P0.04). Children of low EL parents also consumed less water and vegetables compared with their counterparts (all P0.04). In most instances, we found an independent contribution of parental MS and EL to adiposity and eating habits. A more pronounced effect was found if both parents were migrants or of low EL. Differences in adiposity and eating habits were relatively similar to the joint parental data when assessed individually for maternal and paternal MS and EL.Conclusions:Parental MS and EL are independently related to adiposity and various eating habits in preschoolers.European Journal of Clinical Nutrition advance online publication, 3 November 2010; doi:10.1038/ejcn.2010.248.

Linear least squares estimation of the first order moving average parameter

We propose an iterative procedure to minimize the sum of squares function which avoids the nonlinear nature of estimating the first order moving average parameter and provides a closed form of the estimator. The asymptotic properties of the method are discussed and the consistency of the linear least squares estimator is proved for the invertible case. We perform various Monte Carlo experiments in order to compare the sample properties of the linear least squares estimator with its nonlinear counterpart for the conditional and unconditional cases. Some examples are also discussed

Estimating biophysical variable dependences with kernels

This paper introduces a nonlinear measure of dependence between random variables in the context of remote sensing data analysis. The Hilbert-Schmidt Independence Criterion (HSIC) is a kernel method for evaluating statistical dependence. HSIC is based on computing the Hilbert-Schmidt norm of the cross-covariance operator of mapped samples in the corresponding Hilbert spaces. The HSIC empirical estimator is very easy to compute and has good theoretical and practical properties. We exploit the capabilities of HSIC to explain nonlinear dependences in two remote sensing problems: temperature estimation and chlorophyll concentration prediction from spectra. Results show that, when the relationship between random variables is nonlinear or when few data are available, the HSIC criterion outperforms other standard methods, such as the linear correlation or mutual information.