Validating Policy‐Induced Economic Change Using Sequential General Equilibrium SAMs

We present a novel sequential approach that explores the capacity of Computable general equilibrium (CGE) models to track down policy‐induced economic changes and their ability to generate contrastable data. We use an empirical Social accounting matrix (SAM) of the region of Andalusia, in the south of Spain, to construct an initial CGE model. This model is then perturbed with a set of policy shocks related to EU Structural Funds invested into Andalusia. These shocks are accompanied by some parameter adjustments that pick up the main external changes not explained by the model. We generate a sequence of model‐produced virtual SAMs. We then compare the last virtual SAM in the sequence with a new available empirical SAM. This allows us to check relatedness, for the same year, between the model produced and the empirical SAMs. The results show a good fit to the empirical data, providing further support to the CGE modelling tool. Copyright © 2016 John Wiley & Sons, Ltd.     

Forecasting volatility with support vector machine‐based GARCH model

Recently, support vector machine (SVM), a novel artificial neural network (ANN), has been successfully used for financial forecasting. This paper deals with the application of SVM in volatility forecasting under the GARCH framework, the performance of which is compared with simple moving average, standard GARCH, nonlinear EGARCH and traditional ANN‐GARCH models by using two evaluation measures and robust Diebold–Mariano tests. The real data used in this study are daily GBP exchange rates and NYSE composite index. Empirical results from both simulation and real data reveal that, under a recursive forecasting scheme, SVM‐GARCH models significantly outperform the competing models in most situations of one‐period‐ahead volatility forecasting, which confirms the theoretical advantage of SVM. The standard GARCH model also performs well in the case of normality and large sample size, while EGARCH model is good at forecasting volatility under the high skewed distribution. The sensitivity analysis to choose SVM parameters and cross‐validation to determine the stopping point of the recurrent SVM procedure are also examined in this study. Copyright © 2009 John Wiley & Sons, Ltd.     

How to Finance Pensions: Optimal Strategies for Pay‐as‐You‐Go Pension Systems

The aim of this paper was to design optimal strategies using nonlinear dynamic programming to guarantee the required level of liquidity in pay‐as‐you‐go pension systems through changes in the key variables of the system, such as the contribution rate, retirement age and/or indexation of pensions. These strategies, also known as automatic balancing mechanisms (ABMs), calculate the optimal path of these variables over time, managing fluctuations in longevity, fertility rates, salary growth or any other kind of uncertainty faced by the pension scheme without repeated legislative intervention. A numerical application of our model, which uses the projection of the population structure of two representative countries, illustrates the main findings of the paper. Copyright © 2015 John Wiley & Sons, Ltd.     

Involution of genital function with age studied as an index aging in Drosophila melanogaster]

Reproductive system in Drosophila estimated by fertility in female and fertilizing power in male gradually decreased with age. Physiological changes as a function of age occur slowly in virgin females and their ageing seems really slower than those observed in inseminated ones. On the other hand, genital function involution is the same in virgin and mated males and seems independent of aging.     

Estimating and Forecasting APARCH‐Skew‐t Model by Wavelet Support Vector Machines

This paper concentrates on comparing estimation and forecasting ability of quasi‐maximum likelihood (QML) and support vector machines (SVM) for financial data. The financial series are fitted into a family of asymmetric power ARCH (APARCH) models. As the skewness and kurtosis are common characteristics of the financial series, a skew‐t distributed innovation is assumed to model the fat tail and asymmetry. Prior research indicates that the QML estimator for the APARCH model is inefficient when the data distribution shows departure from normality, so the current paper utilizes the semi‐parametric‐based SVM method and shows that it is more efficient than the QML under the skewed Student's‐t distributed error. As the SVM is a kernel‐based technique, we further investigate its performance by applying separately a Gaussian kernel and a wavelet kernel. The results suggest that the SVM‐based method generally performs better than QML for both in‐sample and out‐of‐sample data. The outcomes also highlight the fact that the wavelet kernel outperforms the Gaussian kernel with lower forecasting error, better generation capability and more computation efficiency. Copyright © 2014 John Wiley & Sons, Ltd.     

灰色及其改进模型在人口预测中的应用

在灰色GM（1，1）模型和优化的等维递补GM（1，1）模型的基础上，构建了灰色＋BP神经网络组合模型。对2001～2005年我国人口的变化分析后，建立人口总量模型进行预测，利用原始教据建立的灰色＋BP神经网络组合模型预测我国2008年以后五年的人口总量为13．39亿、13．49亿、13．60亿、13．7亿、13．79亿，有逐年上升的趋势。经综合误差分析和后验差检验均为“优秀”，说明该模型具有一定的应用价值。     

Bounds for the least squares extrapolation in non‐linear AR(1) processes

It is proved that formula for least squares extrapolation in stationary non‐linear AR(1) process is valid also for non‐stationary non‐linear AR(1) processes. This formula depends on the distribution of the corresponding white noise. If the non‐linear function used in the model is non‐decreasing and concave, upper and lower bounds are derived for least squares extrapolation such that the bounds depend only on the expectation of the white noise. It is shown in an example that the derived bounds in some cases give a good approximation to the least squares extrapolation. Copyright © 2001 John Wiley & Sons, Ltd.     

On robust estimation of threshold autoregressions

We investigate the effects of additive outliers on the least squares (LS) estimation of threshold autoregressive models. The class of generalized-M (GM) estimates for linear time series is modified and applied to non-linear threshold processes. A Monte Carlo experiment is carried out to study the robust properties of these estimates. Their relative forecasting performances are also examined. The results indicate that the GM method is preferable to the LS estimation when the observations are contaminated by additive outliers. A real example is also given to illustrate the proposed method.     

Biosynthesis of macromolecules of the intercellular matrix in aorta organ culture. Influence of the age of the animal]

Rabbit aorta organ cultures were used as a model for the study of the modifications of the rate of biosynthesis of intercellular matrix macromolecules with age. Aortas of new born (100 g), young (400 g) and adult (2 kg) rabbits were maintained in culture for several weeks. 14C-lysine is incorporated in all the macromolecular fractions of the aortas, even in polymeric elastin. The change with age of the distribution of proteins of the aorta-explants in successive extracts obtained by a "chemical dissection" procedure and the decrease of their specific radioactivity wtih age enabled us to characterise biochemically the "aging program" of the arterial wall.     

A Bayesian nonlinear support vector machine error correction model

The use of linear error correction models based on stationarity and cointegration analysis, typically estimated with least squares regression, is a common technique for financial time series prediction. In this paper, the same formulation is extended to a nonlinear error correction model using the idea of a kernel‐based implicit nonlinear mapping to a high‐dimensional feature space in which linear model formulations are specified. Practical expressions for the nonlinear regression are obtained in terms of the positive definite kernel function by solving a linear system. The nonlinear least squares support vector machine model is designed within the Bayesian evidence framework that allows us to find appropriate trade‐offs between model complexity and in‐sample model accuracy. From straightforward primal–dual reasoning, the Bayesian framework allows us to derive error bars on the prediction in a similar way as for linear models and to perform hyperparameter and input selection. Starting from the results of the linear modelling analysis, the Bayesian kernel‐based prediction is successfully applied to out‐of‐sample prediction of an aggregated equity price index for the European chemical sector. Copyright © 2006 John Wiley & Sons, Ltd.