Large Vector Auto Regressions

One popular approach for nonstructural economic and financial forecasting is to include a large number of economic and financial variables, which has been shown to lead to significant improvements for forecasting, for example, by the dynamic factor models. A challenging issue is to determine which variables and (their) lags are relevant, especially when there is a mixture of serial correlation (temporal dynamics), high dimensional (spatial) dependence structure and moderate sample size (relative to dimensionality and lags). To this end, an \textit{integrated} solution that addresses these three challenges simultaneously is appealing. We study the large vector auto regressions here with three types of estimates. We treat each variable's own lags different from other variables' lags, distinguish various lags over time, and is able to select the variables and lags simultaneously. We first show the consequences of using Lasso type estimate directly for time series without considering the temporal dependence. In contrast, our proposed method can still produce an estimate as efficient as an \textit{oracle} under such scenarios. The tuning parameters are chosen via a data driven "rolling scheme" method to optimize the forecasting performance. A macroeconomic and financial forecasting problem is considered to illustrate its superiority over existing estimators

Large Vector Auto Regressions

One popular approach for nonstructural economic and financial forecasting is to include a large number of economic and financial variables, which has been shown to lead to significant improvements for forecasting, for example, by the dynamic factor models. A challenging issue is to determine which variables and (their) lags are relevant, especially when there is a mixture of serial correlation (temporal dynamics), high dimensional (spatial) dependence structure and moderate sample size (relative to dimensionality and lags). To this end, an integrated solution that addresses these three challenges simultaneously is appealing. We study the large vector auto regressions here with three types of estimates. We treat each variable's own lags different from other variables' lags, distinguish various lags over time, and is able to select the variables and lags simultaneously. We first show the consequences of using Lasso type estimate directly for time series without considering the temporal dependence. In contrast, our proposed method can still produce an estimate as efficient as an oracle under such scenarios. The tuning parameters are chosen via a data driven "rolling scheme" method to optimize the forecasting performance. A macroeconomic and financial forecasting problem is considered to illustrate its superiority over existing estimators.Time Series, Vector Auto Regression, Regularization, Lasso, Group Lasso, Oracle estimator

Introduction: Reflections on nancy abelmann's legacy

Nancy Abelmann passed away on January 6, 2016, at the age of fifty-six. She received her PhD from the University of California, Berkeley in 1990, after completing her dissertation under Nelson Graburn. That same year, she was hired by the University of Illinois, Urbana-Champaign, where she worked for two and a half decades. She was a beloved teacher, mentor, and colleague to many, and she was a key figure in multiple departments and centers. At the time of her death, she held the Harold E. Preble Professorship in Anthropology, Asian American Studies, East Asian Languages and Cultures, and Women and Gender Studies and was also Associate Vice Chancellor for Research

Pulsar Velocity with Three-Neutrino Oscillations in Non-adiabatic Processes

We have studied the position dependence of neutrino energy on the Kusenko-Segr\`{e} mechanism as an explanation of the proper motion of pulsars. The mechanism is also examined in three-generation mixing of neutrinos and in a non-adiabatic case. The position dependence of neutrino energy requires the higher value of magnetic field such as $B\sim 3\times 10^{15}$ Gauss in order to explain the observed proper motion of pulsars. It is shown that possible non-adiabatic processes decrease the neutrino momentum asymmetry, whereas an excess of electron neutrino flux over other flavor neutrino fluxes increases the neutrino momentum asymmetry. It is also shown that a general treatment with all three neutrinos does not modify the result of the two generation treatment if the standard neutrino mass hierarchy is assumed.Comment: 8 pages, REVTEX, no figure

U-health expert system with statistical neural network

Ubiquitous Health(U-Health) system witch focuses on automated applications that can provide healthcare to human anywhere and anytime using wired and wireless mobile technologies is becoming increasingly important. This system consists of a network system to collect data and a sensor module which measures pulse, blood pressure, diabetes, blood sugar, body fat diet with management and measurement of stress etc, by both wired and wireless and further portable mobile connections. In this paper, we propose an expert system using back-propagation to support the diagnosis of citizens in U-Health system

Effects of Nanoparticle Geometry and Size Distribution on Diffusion Impedance of Battery Electrodes

The short diffusion lengths in insertion battery nanoparticles render the capacitive behavior of bounded diffusion, which is rarely observable with conventional larger particles, now accessible to impedance measurements. Coupled with improved geometrical characterization, this presents an opportunity to measure solid diffusion more accurately than the traditional approach of fitting Warburg circuit elements, by properly taking into account the particle geometry and size distribution. We revisit bounded diffusion impedance models and incorporate them into an overall impedance model for different electrode configurations. The theoretical models are then applied to experimental data of a silicon nanowire electrode to show the effects of including the actual nanowire geometry and radius distribution in interpreting the impedance data. From these results, we show that it is essential to account for the particle shape and size distribution to correctly interpret impedance data for battery electrodes. Conversely, it is also possible to solve the inverse problem and use the theoretical "impedance image" to infer the nanoparticle shape and/or size distribution, in some cases, more accurately than by direct image analysis. This capability could be useful, for example, in detecting battery degradation in situ by simple electrical measurements, without the need for any imaging.Comment: 30 page

The angular two-point correlation of NVSS galaxies revisited

We measure the angular two-point correlation and angular power spectrum from the NRAO VLA Sky Survey (NVSS) of radio galaxies. They are found to be consistent with the best-fit cosmological model from the Planck analysis, and with the redshift distribution obtained from the Combined EIS-NVSS Survey Of Radio Sources (CENSORS). Our analysis is based on an optimal estimation of the two-point correlation function and makes use of a new mask, that takes into account direction dependent effects of the observations, sidelobe effects of bright sources and galactic foreground. We also set a flux threshold and take the cosmic radio dipole into account. The latter turns out to be an essential step in the analysis. This improved cosmological analysis of the NVSS emphasizes the importance of a flux calibration that is robust and stable on large angular scales for future radio continuum surveys.Comment: 11 pages, 15 figure

On a Conjecture of Givental

These brief notes record our puzzles and findings surrounding Givental's recent conjecture which expresses higher genus Gromov-Witten invariants in terms of the genus-0 data. We limit our considerations to the case of a projective line, whose Gromov-Witten invariants are well-known and easy to compute. We make some simple checks supporting his conjecture.Comment: 13 pages, no figures; v.2: new title, minor change