Bootstrap prediction mean squared errors of unobserved states based on the Kalman filter with estimated parameters

Prediction intervals in State Space models can be obtained by assuming Gaussian innovations and using the prediction equations of the Kalman filter, where the true parameters are substituted by consistent estimates. This approach has two limitations. First, it does not incorporate the uncertainty due to parameter estimation. Second, the Gaussianity assumption of future innovations may be inaccurate. To overcome these drawbacks, Wall and Stoffer (2002) propose to obtain prediction intervals by using a bootstrap procedure that requires the backward representation of the model. Obtaining this representation increases the complexity of the procedure and limits its implementation to models for which it exists. The bootstrap procedure proposed by Wall and Stoffer (2002) is further complicated by fact that the intervals are obtained for the prediction errors instead of for the observations. In this paper, we propose a bootstrap procedure for constructing prediction intervals in State Space models that does not need the backward representation of the model and is based on obtaining the intervals directly for the observations. Therefore, its application is much simpler, without loosing the good behavior of bootstrap prediction intervals. We study its finite sample properties and compare them with those of the standard and the Wall and Stoffer (2002) procedures for the Local Level Model. Finally, we illustrate the results by implementing the new procedure to obtain prediction intervals for future values of a real time series

GARCH models with leverage effect : differences and similarities

In this paper, we compare the statistical properties of some of the most popular GARCH models with leverage e?ect when their parameters satisfy the positivity, stationarity and nite fourth order moment restrictions. We show that the EGARCH speci cation is the most exible while the GJR model may have important limitations when restricted to have nite kurtosis. On the other hand, we show empirically that the conditional standard deviations estimated by the TGARCH and EGARCH models are almost identical and very similar to those estimated by the APARCH model. However, the estimates of the QGARCH and GJR models di?er among them and with respect to the other three speci cations

Bootstrap prediction mean squared errors of unobserved states based on the Kalman filter with estimated parameters

Prediction intervals in State Space models can be obtained by assuming Gaussian innovations and using the prediction equations of the Kalman filter, where the true parameters are substituted by consistent estimates. This approach has two limitations. First, it does not incorporate the uncertainty due to parameter estimation. Second, the Gaussianity assumption of future innovations may be inaccurate. To overcome these drawbacks, Wall and Stoffer (2002) propose to obtain prediction intervals by using a bootstrap procedure that requires the backward representation of the model. Obtaining this representation increases the complexity of the procedure and limits its implementation to models for which it exists. The bootstrap procedure proposed by Wall and Stoffer (2002) is further complicated by fact that the intervals are obtained for the prediction errors instead of for the observations. In this paper, we propose a bootstrap procedure for constructing prediction intervals in State Space models that does not need the backward representation of the model and is based on obtaining the intervals directly for the observations. Therefore, its application is much simpler, without loosing the good behavior of bootstrap prediction intervals. We study its finite sample properties and compare them with those of the standard and the Wall and Stoffer (2002) procedures for the Local Level Model. Finally, we illustrate the results by implementing the new procedure to obtain prediction intervals for future values of a real time series.NAIRU, Output gap, Parameter uncertainty, Prediction Intervals, State Space Models

A POWERFUL TEST FOR CONDITIONAL HETEROSCEDASTICITY FOR FINANCIAL TIME SERIES WITH HIGHLY PERSISTENT VOLATILITIES.

Traditional tests for conditional heteroscedasticity are based on testing for significant autocorrelations of squared or absolute observations. In the context of high frequency time series of financial returns, these autocorrelations are often positive and very persistent, although their magnitude is usually very small. Moreover, the sample autocorrelations are severely biased towards zero, specially if the volatility is highly persistent. Consequently, the power of the traditional tests is often very low. In this paper, we propose a new test that takes into account not only the magnitude of the sample autocorrelations but also possible patterns among them. This aditional information makes the test more powerful in situations of empirical interest. The asymptotic distribution of the new statistic is derived and its finite sample properties are analized by means of Monte Carlo experiments. The performance of the new test is compared with other alternative tests. Finally, we illustrate the results analysing several real time series of financial returns.

Aberrant meiotic behavior in Agave tequilana Weber var. azul

BACKGROUND: Agave tequilana Weber var. azul, is the only one variety permitted by federal law in México to be used for tequila production which is the most popular contemporary alcoholic beverage made from agave and recognized worldwide. Despite the economic, genetic, and ornamental value of the plant, it has not been subjected to detailed cytogenetic research, which could lead to a better understanding of its reproduction for future genetic improvement. The objective of this work was to study the meiotic behavior in pollen mother cells and its implications on the pollen viability in Agave tequilana Weber var. azul. RESULTS: The analysis of Pollen Mother Cells in anaphase I (A-I) showed 82.56% of cells with a normal anaphase and, 17.44% with an irregular anaphase. In which 5.28% corresponded to cells with side arm bridges (SAB); 3.68% cells with one bridge and one fragment; 2.58% of irregular anaphase showed cells with one or two lagging chromosomes and 2.95% showed one acentric fragment; cells with two bridges and cells with two bridges and one acentric fragment were observed in frequencies of 1.60% and 1.35% respectively. In anaphase II some cells showed bridges and fragments too. Aberrant A-I cells had many shrunken or empty pollen grains (42.00%) and 58.00 % viable pollen. CONCLUSION: The observed meiotic irregularities suggest that structural chromosome aberrations have occurred, such as heterozygous inversions, sister chromatid exchanges, deletions and duplications which in turn are reflected in a low pollen viability

GARCH models with leverage effect : differences and similarities

In this paper, we compare the statistical properties of some of the most popular GARCH models with leverage effect when their parameters satisfy the positivity, stationarity and nite fourth order moment restrictions. We show that the EGARCH specication is the most exible while the GJR model may have important limitations when restricted to have nite kurtosis. On the other hand, we show empirically that the conditional standard deviations estimated by the TGARCH and EGARCH models are almost identical and very similar to those estimated by the APARCH model. However, the estimates of the QGARCH and GJR models differ among them and with respect to the other three specications.EGARCH, GJR, QGARCH, TGARCH, APARCH

A power efficient neural spike recording channel with data bandwidth reduction

This paper presents a mixed-signal neural spike recording channel which features, as an added value, a simple and low-power data compression mechanism. The channel uses a band-limited differential low noise amplifier and a binary search data converter, together with other digital and analog blocks for control, programming and spike characterization. The channel offers a self-calibration operation mode and it can be configured both for signal tracking (to raw digitize the acquired neural waveform) and feature extraction (to build a first-order PWL approximation of the spikes). The prototype has been fabricated in a standard CMOS 0.13μm and occupies 400μm×400μm. The overall power consumption of the channel during signal tracking is 2.8μW and increases to 3.0μW average when the feature extraction operation mode is programmed.Ministerio de Ciencia e Innovación TEC2009-08447Junta de Andalucía TIC-0281

"Hiding our faces to be seen": strategies of visibility of activism

Presentación en formato pósterThis paper explores how this gesture of masking, hiding, and facial-covering is not only a liberation response to a form of oppression or a political statement against some unlawful action; but that it also works as a strategy of visibility. Following the research of Eesley, DeCelles and Lenox (2015) focusing on activist types and tactics and Bennet (2003), about global activism and networked politics, we explore the strategic communication component behind the visibility in these protests. As Ciszek states “activism is a form of strategic communication” (2017, p.702). Combining a series of quantitative and qualitative techniques for its analysis, this paper brings together recent forms of “masked activism” around the world. We elaborate a typology that helps for the understanding of the strategies and actions happening in activism and social movements with the use of masks. The question deriving from this principle would be then, to what extent those actions and strategies are ideologically grounded: can these strategies and actions differ from movements to movements? And in particular, what does the mask do, in each case? Is a mask more than a mask? Exploring these aspects should enable further research on social movements and political activism from the strategic and communicative organisation.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Universidad de Málaga (España) - Sheffield Hallam University (Reino Unido

WMT 2016 Multimodal translation system description based on bidirectional recurrent neural networks with double-embeddings

Bidirectional Recurrent Neural Networks (BiRNNs) have shown outstanding results on sequence-to-sequence learning tasks. This architecture becomes specially interesting for multimodal machine translation task, since BiRNNs can deal with images and text. On most translation systems the same word embedding is fed to both BiRNN units. In this paper, we present several experiments to enhance a baseline sequence-to-sequence system (Elliott et al., 2015), for example, by using double embeddings. These embeddings are trained on the forward and backward direction of the input sequence. Our system is trained, validated and tested on the Multi30K dataset (Elliott et al., 2016) in the context of theWMT 2016Multimodal Translation Task. The obtained results show that thedouble-embedding approach performs significantly better than the traditional single-embedding one.Postprint (published version

Neural Network Based Min-Max Predictive Control. Application to a Heat Exchanger

IFAC Adaptation and Learning in Control and Signal Processing. Cemobbio-Como. Italy. 2001Min-max model predictive controllers (MMMPC) have been proposed for the control of linear plants subject to bounded uncertainties. The implementation of MMMPC suffers a large computational burden due to the numerical optimization problem that has to be solved at every sampling time. This fact severely limits the class of processes in which this control is suitable. In this paper the use of a Neural Network (NN) to approximate the solution of the min-max problem is proposed. The number of inputs of the NN is determined by the order and time delay of the model together with the control horizon. For large time delays the number of inputs can be prohibitive. A modification to the basic formulation is proposed in order to avoid this later problem. Simulation and experimental results are given using a heat exchanger