New in-sample prediction errors in time series with applications

^aThis article introduces two new types of prediction errors in time series: the filtered prediction errors and the deletion prediction errors. These two prediction errors are obtained in the same sample used for estimation, but in such a way that they share some common properties with out of sample prediction errors. It is proved that the filtered prediction errors are uncorrelated, up to terms of magnitude order O(T^-2), with the in sample innovations, a property that share with the out-of-sample prediction errors. On the other hand, deletion prediction errors assume that the values to be predicted are unobserved, a property that they also share with out-of-sample prediction errors. It is shown that these prediction errors can be computed with parameters estimated by assuming innovative or additive outliers, respectively, at the points to be predicted. Then the prediction errors are obtained by running the procedure for all the points in the sample of data. Two applications of these new prediction errors are presented. The first is the estimation and comparison of the prediction mean squared errors of competing predictors. The second is the determination of the order of an ARMA model. In the two applications the proposed filtered prediction errors have some advantages over alternative existing methods.

Multiple Scattering Formulation of Two Dimensional Acoustic and Electromagnetic Metamaterials

This work presents a multiple scattering formulation of two dimensional acoustic metamaterials. It is shown that in the low frequency limit multiple scattering allows us to define frequency-dependent effective acoustic parameters for arrays of both ordered and disordered cylinders. This formulation can lead to both positive and negative acoustic parameters, where the acoustic parameters are the scalar bulk modulus and the tensorial mass density and, therefore, anisotropic wave propagation is allowed with both positive or negative refraction index. It is also shown that the surface fields on the scatterer are the main responsible of the anomalous behavior of the effective medium, therefore complex scatterers can be used to engineer the frequency response of the effective medium, and some examples of application to different scatterers are given. Finally, the theory is extended to electromagnetic wave propagation, where Mie resonances are found to be the responsible of the metamaterial behavior. As an application, it is shown that it is possible to obtain metamaterials with negative permeability and permittivity tensors by arrays of all-dielectric cylinders and that anisotropic cylinders can tune the frequency response of these resonances

PROPERTIES OF PREDICTORS IN OVERDIFFERENCED NEARLY NONSTATIONARY AUTOREGRESSION

This paper analyzes the effect of overdifferencing a stationary AR(p+1) process whoselargest root is near unity. It is found that if the process is nearly nonstationary, the estimators ofthe overdifferenced model ARIMA (p, 1, 0) are root-T consistent. It is also found that thismisspecified ARIMA (p, 1, 0) has lower predictive mean squared error, to terms of small order,that the properly specified AR(p+1) model due to its parsimony. The advantage of theoverdifferenced predictor depends on the remaining roots, the prediction horizon, and the meanof the process.Autoregressive processes, near nonstationarity, overdifferencing

NEW IN-SAMPLE PREDICTION ERRORS IN TIME SERIES WITH APPLICATIONS

This article introduces two new types of prediction errors in time series: the filtered prediction errors and the deletion prediction errors. These two prediction errors are obtained in the same sample used for estimation, but in such a way that they share some common properties with out of sample prediction errors. It is proved that the filtered prediction errors are uncorrelated, up to terms of magnitude order O(T-2), with the in sample innovations, a property that share with the out-of-sample prediction errors. On the other hand, deletion prediction errors assume that the values to be predicted are unobserved, a property that they also share with out-of-sample prediction errors. It is shown that these prediction errors can be computed with parameters estimated by assuming innovative or additive outliers, respectively, at the points to be predicted. Then the prediction errors are obtained by running the procedure for all the points in the sample of data. Two applications of these new prediction errors are presented. The first is the estimation and comparison of the prediction mean squared errors of competing predictors. The second is the determination of the order of an ARMA model. In the two applications the proposed filtered prediction errors have some advantages over alternative existing methods..

Symbolic representation of scenarios in Bologna airport on virtual reality concept

This paper is a part of a big Project named Retina Project, which is focused in reduce the workload of an ATCO. It uses the last technological advances as Virtual Reality concept. The work has consisted in studying the different awareness situations that happens daily in Bologna Airport. It has been analysed one scenario with good visibility where the sun predominates and two other scenarios with poor visibility where the rain and the fog dominate. Due to the study of visibility in the three scenarios computed, the conclusion obtained is that the overlay must be shown with a constant dimension regardless the position of the aircraft to be readable by the ATC and also, the frame and the flight strip should be coloured in a showy colour (like red) for a better control by the ATCO

The Unemployment Benefit System: a Redistributive or an Insurance Institution?

We analyze the effects of the unemployment benefit system on the economy. In particular, we focus on both the tax structure and the unemployment benefits composition. We show that if the unemployment benefit system is only paid by firms, then employment and production are maximized. Moreover, the way the government contemplates the unemployment benefit system, either as a redistributive or as an insurance institution, is crucial for the dynamics and the equilibria of the economy.unemployment benefit system, payroll tax, wage tax.