121,662 research outputs found
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
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
Recommended from our members
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
VISHNU hybrid model for viscous QCD matter at RHIC and LHC energies
In this proceeding, we briefly describe the viscous hydrodynamics + hadron
cascade hybrid model VISHNU for relativistic heavy ion collisions and report
the current status on extracting the QGP viscosity from elliptic flow data.Comment: 4 pages, 1 figure, the proceedings of 7th International Workshop on
Critical Point and Onset of Deconfinement, Wuhan, China, Nov. 7-11, 201
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
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 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
- …