Monetary policy with signal extraction from the bond market

Monetary policy is conducted in an environment of uncertainty. This paper sets upa model where the central bank uses real-time data from the bond market togetherwith standard macroeconomic indicators to estimate the current state of theeconomy more efficiently, while taking into account that its own actions influencewhat it observes. The timeliness of bond market data allows for quicker responsesof monetary policy to disturbances compared to the case when the central bankhas to rely solely on collected aggregate data. The information content of theterm structure creates a link between the bond market and the macroeconomythat is novel to the literature. To quantify the importance of the bond market asa source of information, the model is estimated on data for the United Statesand Australia using Bayesian methods. The empirical exercise suggests that thereis some information in the US term structure that helps the Federal Reserve toidentify shocks to the economy on a timely basis. Australian bond prices seemto be less informative than their US counterparts, perhaps because Australia is arelatively small and open economy.

Statistical signal extraction using stable processes

The standard models for statistical signal extraction assume that the signal and noise are generated by linear Gaussian processes. The optimum filter weights for those models are derived using the method of minimum mean square error. In the present work we study the properties of signal extraction models under the assumption that signal/noise are generated by symmetric stable processes. The optimum filter is obtained by the method of minimum dispersion. The performance of the new filter is compared with their Gaussian counterparts by simulation.

Giffen goods and market making

This paper shows that information effects per se are not responsible forthe Giffen goods anomaly affecting competitive traders demands in multi-asset, noisy rational expectations equilibrium models. The role thatinformation plays in traders strategies also matters. In a market withrisk averse, uninformed traders, informed agents havea dual motive for trading: speculation and market making. Whilespeculation entails using prices to assess the effect of private signalerror terms, market making requires employing them to disentangle noisetraders effects in traders aggregate orders. In a correlated environment,this complicates a trader s signal-extraction problem and maygenerate upward-sloping demand curves. Assuming either (i) that competitive,risk neutral market makers price the assets, or that (ii) the risktolerance coefficient of uninformed traders grows without bound, removesthe market making component from informed traders demands, rendering themwell behaved in prices.

Irreversible investment in stochastically cyclical markets

This paper presents a new framework for studying irreversible (dis)investment whena market follows a random number of random-length cycles (such as a high-tech productmarket). It is assumed that a firm facing such market evolution is always unsure aboutwhether the current cycle is the last one, although it can update its beliefs about theprobability of facing a permanent decline by observing that no further growth phasearrives. We show that the existence of regime shifts in fluctuating markets suffices for anoption value of waiting to (dis)invest to arise, and we provide a marginal interpretationof the optimal (dis)investment policies, absent in the real options literature. Thepaper also shows that, despite the stochastic process of the underlying variable has acontinuous sample path, the discreteness in the regime changes implies that the samplepath of the firm s value experiences jumps whenever the regime switches all of a sudden,irrespective of whether the firm is active or not.

Modeling and Analysis of Some Heavy Tailed Time Series

The thesis has covered various aspects of modeling and analysis of finite mean time series with symmetric stable distributed innovations. Time series analysis based on Box and Jenkins methods are the most popular approaches where the models are linear and errors are Gaussian. We highlighted the limitations of classical time series analysis tools and explored some generalized tools and organized the approach parallel to the classical set up. In the present thesis we mainly studied the estimation and prediction of signal plus noise model. Here we assumed the signal and noise follow some models with symmetric stable innovations.We start the thesis with some motivating examples and application areas of alpha stable time series models. Classical time series analysis and corresponding theories based on finite variance models are extensively discussed in second chapter. We also surveyed the existing theories and methods correspond to infinite variance models in the same chapter. We present a linear filtering method for computing the filter weights assigned to the observation for estimating unobserved signal under general noisy environment in third chapter. Here we consider both the signal and the noise as stationary processes with infinite variance innovations. We derived semi infinite, double infinite and asymmetric signal extraction filters based on minimum dispersion criteria. Finite length filters based on Kalman-Levy filters are developed and identified the pattern of the filter weights. Simulation studies show that the proposed methods are competent enough in signal extraction for processes with infinite variance.Parameter estimation of autoregressive signals observed in a symmetric stable noise environment is discussed in fourth chapter. Here we used higher order Yule-Walker type estimation using auto-covariation function and exemplify the methods by simulation and application to Sea surface temperature data. We increased the number of Yule-Walker equations and proposed a ordinary least square estimate to the autoregressive parameters. Singularity problem of the auto-covariation matrix is addressed and derived a modified version of the Generalized Yule-Walker method using singular value decomposition.In fifth chapter of the thesis we introduced partial covariation function as a tool for stable time series analysis where covariance or partial covariance is ill defined. Asymptotic results of the partial auto-covariation is studied and its application in model identification of stable auto-regressive models are discussed. We generalize the Durbin-Levinson algorithm to include infinite variance models in terms of partial auto-covariation function and introduce a new information criteria for consistent order estimation of stable autoregressive model.In chapter six we explore the application of the techniques discussed in the previous chapter in signal processing. Frequency estimation of sinusoidal signal observed in symmetric stable noisy environment is discussed in this context. Here we introduced a parametric spectrum analysis and frequency estimate using power transfer function. Estimate of the power transfer function is obtained using the modified generalized Yule-Walker approach. Another important problem in statistical signal processing is to identify the number of sinusoidal components in an observed signal. We used a modified version of the proposed information criteria for this purpose.

Object-Space Road Extraction in Rural Areas Using Stereoscopic Aerial Images

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Search for s-channel single top-quark production in proton-proton collisions at s√=8 TeV with the ATLAS detector

This Letter presents a search at the LHC for s-channel single top-quark production in proton-proton collisions at a centre-of-mass energy of 8 TeV. The analyzed data set was recorded by the ATLAS detector and corresponds to an integrated luminosity of 20.3 fb−1. Selected events contain one charged lepton, large missing transverse momentum and exactly two b-tagged jets. A multivariate event classifier based on boosted decision trees is developed to discriminate s-channel single top-quark events from the main background contributions. The signal extraction is based on a binned maximum-likelihood fit of the output classifier distribution. The analysis leads to an upper limit on the s-channel single top-quark production cross-section of 14.6 pb at the 95% confidence level. The fit gives a cross-section of σs=5.0±4.3 pb, consistent with the Standard Model expectation.

Multiple filtering devices for the estimation of cyclical DSGE models

We propose a method to estimate time invariant cyclical DSGE models using the informationprovided by a variety of filters. We treat data filtered with alternative procedures as contaminated proxies of the relevant model-based quantities and estimate structural and non-structuralparameters jointly using a signal extraction approach. We employ simulated data to illustratethe properties of the procedure and compare our conclusions with those obtained when just onefilter is used. We revisit the role of money in the transmission of monetary business cycles.

¿Son excesivamente suaves las series de contabilidad nacional trimestral?

Este trabajo analiza si las series de Contabilidad Nacional Trimestral de España son excesivamente suaves y, por lo tanto, si son realmente informativas de la evolución de la economía española a corto plazo. Mediante la utilización de las técnicas de análisis espectral se observa que las series trimestrales españolas presentan una variabilidad mayor que las de otros países de la OCDE en el intervalo de frecuencias más bajas (asociadas al comportamiento de la serie a largo plazo ) y una variabilidad menor en el intervalo de frecuencias más altas (asociadas al ruido que contiene la serie). El motivo de este comportamiento diferencial de las series trimestrales españolas se encuentra en el método utilizado por el Instituto Nacional de Estadística por estimar la señal ciclo-tendencia de los indicadores utilizados como referencia, concretamente, el conocido como filtro de líneas aéreas modificado (LAM)

¿Son excesivamente suaves las series de contabilidad nacional trimestral?

Este trabajo analiza si las series de Contabilidad Nacional Trimestral de España son excesivamente suaves y, por lo tanto, si son realmente informativas de la evolución de la economía española a corto plazo. Mediante la utilización de las técnicas de análisis espectral se observa que las series trimestrales españolas presentan una variabilidad mayor que las de otros países de la OCDE en el intervalo de frecuencias más bajas (asociadas al comportamiento de la serie a largo plazo ) y una variabilidad menor en el intervalo de frecuencias más altas (asociadas al ruido que contiene la serie). El motivo de este comportamiento diferencial de las series trimestrales españolas se encuentra en el método utilizado por el Instituto Nacional de Estadística por estimar la señal ciclo-tendencia de los indicadores utilizados como referencia, concretamente, el conocido como filtro de líneas aéreas modificado (LAM)

Multiway Array Decomposition Analysis of EEGs in Alzheimer’s Disease

Methods for the extraction of features from physiological datasets are growing needs as clinical investigations of Alzheimer’s disease (AD) in large and heterogeneous population increase. General tools allowing diagnostic regardless of recording sites, such as different hospitals, are essential and if combined to inexpensive non-invasive methods could critically improve mass screening of subjects with AD. In this study, we applied three state of the art multiway array decomposition (MAD) methods to extract features from electroencephalograms (EEGs) of AD patients obtained from multiple sites. In comparison to MAD, spectral-spatial average filter (SSFs) of control and AD subjects were used as well as a common blind source separation method, algorithm for multiple unknown signal extraction (AMUSE). We trained a feed-forward multilayer perceptron (MLP) to validate and optimize AD classification from two independent databases. Using a third EEG dataset, we demonstrated that features extracted from MAD outperformed features obtained from SSFs AMUSE in terms of root mean squared error (RMSE) and reaching up to 100% of accuracy in test condition. We propose that MAD maybe a useful tool to extract features for AD diagnosis offering great generalization across multi-site databases and opening doors to the discovery of new characterization of the disease.

Seletividade espacial de cor do potencial cortical provocado visual pseudoaleatório

A seletividade espacial para cor tem sido investigada usando métodos eletrofisiológicos invasivos e não invasivos, e métodos psicofísicos. Em eletrofisiologia cortical visual não invasiva este tópico foi investigado usando métodos convencionais de estimulação periódica e extração de respostas por promediação simples. Novos métodos de estimulação (apresentação pseudo-aleatória) e extração de respostas corticais não invasivas (correlação cruzada) foram desenvolvidos e ainda não foram usados para investigar a seletividade espacial de cor de respostas corticais. Este trabalho objetivou introduzir esse novo método de eletrofisiologia pseudoaleatória para estudar a seletividade espacial de cor. Foram avaliados 14 tricromatas e 16 discromatópsicos com acuidade visual normal ou corrigida. Os voluntários foram avaliados pelo anomaloscópio HMC e teste de figuras de Ishihara para caracterizar a visão de cores quanto à presença de tricromacia. Foram usadas redes senoidais, 8º de ângulo visual, vermelho-verde para 8 frequências espaciais entre 0,2 a 10 cpg. O estímulo foi temporalmente modulado por uma sequência-m binária em um modo de apresentação de padrão reverso. O sistema VERIS foi usado para extrair o primeiro e o segundo slice do kernel de segunda ordem (K2.1 e K2.2, respectivamente). Após a modelagem da resposta às frequências espaciais com função de diferença de gaussianas, extraiu-se a frequência espacial ótima e banda de frequências com amplitudes acima de ¾ da amplitude máxima da função para servirem como indicadores da seletividade espacial da função. Também foi estimada a acuidade visual cromática pelo ajuste de uma função linear aos dados de amplitude a partir da frequência espacial do pico de amplitude até a mais alta frequência espacial testada. Em tricromatas, foi encontrada respostas cromáticas no K2.1 e no K2.2 que apresentaram seletividade espacial diferentes. Os componentes negativos do K2.1 e do K2.2 apresentaram sintonia passa-banda e o componente positivo do K2.1 apresentou sintonia passa-baixa. A acuidade visual estimada de todos os componentes estudados foi próxima àquelas encontradas por Mullen (1985) e Kelly (1983). Diferentes componentes celulares podem estar contribuindo para a geração do VECP pseudoaleatório. Este novo método se candidata a ser uma importante ferramenta para a avaliação não invasiva da visão de cores em humanos.

A brief look at the least-squares approach as a classifier applied to restricted-vocabulary speech recognition

In this letter, a speech recognition algorithm based on the least-squares method is presented. Particularly, the intention is to exemplify how such a traditional numerical technique can be applied to solve a signal processing problem that is usually treated by using more elaborated formulations.

Approximating method of frames

In this paper, the well-known method of frames approach to the signal decomposition problem is reformulated as a certain bilevel goal-attainment linear least squares problem. As a consequence, a numerically robust variant of the method, named approximating method of frames, is proposed on the basis of a certain minimal Euclidean norm approximating splitting pseudo-iteration-wise method.