On Linearly Constrained Minimum Variance Beamforming

Beamforming is a widely used technique for source localization in signal processing and neuroimaging. A number of vector-beamformers have been introduced to localize neuronal activity by using magnetoencephalography (MEG) data in the literature. However, the existing theoretical analyses on these beamformers have been limited to simple cases, where no more than two sources are allowed in the associated model and the theoretical sensor covariance is also assumed known. The information about the effects of the MEG spatial and temporal dimensions on the consistency of vector-beamforming is incomplete. In the present study, we consider a class of vector-beamformers defined by thresholding the sensor covariance matrix, which include the standard vector-beamformer as a special case. A general asymptotic theory is developed for these vector-beamformers, which shows the extent of effects to which the MEG spatial and temporal dimensions on estimating the neuronal activity index. The performances of the proposed beamformers are assessed by simulation studies. Superior performances of the proposed beamformers are obtained when the signal-to-noise ratio is low. We apply the proposed procedure to real MEG datasets derived from five sessions of a human face-perception experiment, finding several highly active areas in the brain. A good agreement between these findings and the known neurophysiology of the MEG response to human face perception is shown

Low interest rates and housing booms: the role of capital inflows, monetary policy and financial innovation

A number of OECD countries experienced an environment of low interest rates and a rapid Increase in real house prices and residential investment during the past decade. Different explanations have been suggested for the housing boom: expansionary monetary policy, capital inflows due to a global savings glut and excessive financial innovation combined with inappropriately lax financial regulation. In this study we examine the effects of these three factors on the housing market. We estimate a panel VAR for a sample of OECD countries and identify monetary policy and capital inflows shocks using sign restrictions. To explore how the effects of these shocks change with the structure of the mortgage market and the degree of securitization, we allow the VAR coefficients to vary with mortgage market characteristics. Our results suggest that both types of shocks have a significant and positive effect on real house prices, real credit to the private sector and residential investment. The response of housing variables to both types of shocks is stronger in countries with more developed mortgage markets. The amplification effect of mortgage-backed securitization is particularly strong for capital inflows shocks.Money supply ; Capital movements

The effect of short ray trajectories on the scattering statistics of wave chaotic systems

In many situations, the statistical properties of wave systems with chaotic classical limits are well-described by random matrix theory. However, applications of random matrix theory to scattering problems require introduction of system specific information into the statistical model, such as the introduction of the average scattering matrix in the Poisson kernel. Here it is shown that the average impedance matrix, which also characterizes the system-specific properties, can be expressed in terms of classical trajectories that travel between ports and thus can be calculated semiclassically. Theoretical results are compared with numerical solutions for a model wave-chaotic system

High Spatial Resolution Thermal-Infrared Spectroscopy with ALES: Resolved Spectra of the Benchmark Brown Dwarf Binary HD 130948BC

We present 2.9-4.1 micron integral field spectroscopy of the L4+L4 brown dwarf binary HD 130948BC, obtained with the Arizona Lenslets for Exoplanet Spectroscopy (ALES) mode of the Large Binocular Telescope Interferometer (LBTI). The HD 130948 system is a hierarchical triple system, in which the G2V primary is joined by two co-orbiting brown dwarfs. By combining the age of the system with the dynamical masses and luminosities of the substellar companions, we can test evolutionary models of cool brown dwarfs and extra-solar giant planets. Previous near-infrared studies suggest a disagreement between HD 130948BC luminosities and those derived from evolutionary models. We obtained spatially-resolved, low-resolution (R~20) L-band spectra of HD 130948B and C to extend the wavelength coverage into the thermal infrared. Jointly using JHK photometry and ALES L-band spectra for HD 130948BC, we derive atmospheric parameters that are consistent with parameters derived from evolutionary models. We leverage the consistency of these atmospheric quantities to favor a younger age (0.50 \pm 0.07 Gyr) of the system compared to the older age (0.79 \pm 0.22 Gyr) determined with gyrochronology in order to address the luminosity discrepancy.Comment: 17 pages, 9 figures, Accepted to Ap

Dynamic Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances

We propose a new class of models specifically tailored for spatio-temporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and heteroskedastic disturbances, i.e. SARAR(1,1), by exploiting the recent advancements in Score Driven (SD) models typically used in time series econometrics. In particular, we allow for time-varying spatial autoregressive coefficients as well as time-varying regressor coefficients and cross-sectional standard deviations. We report an extensive Monte Carlo simulation study in order to investigate the finite sample properties of the Maximum Likelihood estimator for the new class of models as well as its flexibility in explaining several dynamic spatial dependence processes. The new proposed class of models are found to be economically preferred by rational investors through an application in portfolio optimization.Comment: 33 pages, 5 figure

Assessment of Models of Galactic Thermal Dust Emission Using COBE/FIRAS and COBE/DIRBE Observations

Accurate modeling of the spectrum of thermal dust emission at millimeter wavelengths is important for improving the accuracy of foreground subtraction for CMB measurements, for improving the accuracy with which the contributions of different foreground emission components can be determined, and for improving our understanding of dust composition and dust physics. We fit four models of dust emission to high Galactic latitude COBE/FIRAS and COBE/DIRBE observations from 3 millimeters to 100 microns and compare the quality of the fits. We consider the two-level systems model because it provides a physically motivated explanation for the observed long wavelength flattening of the dust spectrum and the anticorrelation between emissivity index and dust temperature. We consider the model of Finkbeiner, Davis, and Schlegel because it has been widely used for CMB studies, and the generalized version of this model recently applied to Planck data by Meisner and Finkbeiner. For comparison we have also fit a phenomenological model consisting of the sum of two graybody components. We find that the two-graybody model gives the best fit and the FDS model gives a significantly poorer fit than the other models. The Meisner and Finkbeiner model and the two-level systems model remain viable for use in Galactic foreground subtraction, but the FIRAS data do not have sufficient signal-to-noise ratio to provide a strong test of the predicted spectrum at millimeter wavelengths.Comment: 17 pages, 7 figures. Accepted for publication in Ap

Essays on Risk Measurement and Modeling in Macroeconomics and Finance

This dissertation consists of four essays that focus on the measurement and economic analysis of key risk factors behind macroeconomic and financial variables using state-space models. Chapters 2, 3, 4, and 5 develop and implement estimation approaches that can handle nonlinear linkages of economic forces and tackle issues when data are missing or contaminated by errors. Chapter 2 estimates an equilibrium term structure model that includes real and nominal uncertainty in particular that allows for changes in the responsiveness of the Federal Reserve to inflation fluctuations. These uncertainty, particularly those concerning monetary policy action are considered potential sources of risk variations that can explain several features in the U.S. government bond market including the upward sloping yield curve. Chapter 3, co-authored with Frank Schorfheide and Amir Yaron, develops a nonlinear state-space model to estimate predictable mean and volatility components in monthly consumption growth using a mixed-frequency data and accounting for serially-correlated measurement errors. We provide a methodological contribution that allow to maximize the span of the estimation sample to recover the predictable component and at the same time use high-frequency data to efficiently identify the volatility processes. The estimation provides strong evidence for predictable mean and volatility components in consumption growth. We show that the model can go a long way in explaining several well known asset pricing facts of the data. Chapter 4, co-authored with Boragan Aruoba, Francis Diebold, Jeremy Nalewaik, and Frank Schorfheide, considers the fundamental question of GDP estimation, focusing on the U.S., and provides estimates superior to the ubiquitous expenditure-side series by applying optimal signal-extraction techniques to the noisy expenditure-side and income-side GDP estimates. The quarter-by-quarter values of the new measure often differ noticeably from those of the traditional measures, and dynamic properties differ as well, indicating that the persistence of aggregate output dynamics is stronger than previously thought. Chapter 5, co-authored with Frank Schorfheide, develops the idea of using mixed-frequency data in state-space form. We show that adding monthly observations to a quarterly VAR, which then is estimated with Bayesian methods under a Minnesota-style prior, substantially improves its forecasting performance