92 research outputs found
Numerical Distribution Functions for Seasonal Unit Root Tests
When working with time series data observed at intervals smaller than a year, it is often necessary to test for the presence of seasonal unit roots. One of the most widely used methods for testing seasonal unit roots is that of HEGY, which provides test statistics with non-standard distributions. This paper describes a generalisation of this method for any periodicity and uses a response surface regressions approach to calculate the critical values and P values of the HEGY statistics whatever the periodicity and sample size of the data. The algorithms are prepared with the Gretl open source econometrics package and some new tables of critical values for daily, hourly and half-hourly data are presented.seasonality, unit roots, surface response analysis
A Hardware Efficient Random Number Generator for Nonuniform Distributions with Arbitrary Precision
Nonuniform random numbers are key for many technical applications, and designing efficient hardware implementations of non-uniform random
number generators is a very active research field. However, most state-of-the-art architectures are either tailored to specific distributions or use up a lot of hardware resources. At ReConFig 2010, we have presented a new design that saves up to 48% of area compared to state-of-the-art inversion-based implementation, usable for arbitrary distributions and precision. In this paper, we introduce a more flexible version together with a refined segmentation scheme that allows to further reduce the approximation error significantly. We provide a free software tool allowing users to implement their own distributions easily, and we have tested our random number generator thoroughly by statistic analysis and two application tests
Numerical Distribution Functions for Seasonal Unit Root Tests
When working with time series data observed at intervals smaller than a year, it is often necessary to test for the presence of seasonal unit roots. One of the most widely used methods for testing seasonal unit roots is that of HEGY, which provides test statistics with non-standard distributions. This paper describes a generalisation of this method for any periodicity and uses a response surface regressions approach to calculate the critical values and P values of the HEGY statistics whatever the periodicity and sample size of the data. The algorithms are prepared with the Gretl open source econometrics
package and some new tables of critical values for daily, hourly and half-hourly data are presented.Financial support from research project ECO2010-15332 from Ministerio de Ciencia e Innovación, and Econometrics Research Group IT-334-07 from the Basque Government are gratefully acknowledged. The SGI/IZO-SGIker UPV/EHU is gratefully aknowledged for its generous allocation of computational resource
Mixtures of strongly interacting bosons in optical lattices
We investigate the properties of strongly interacting heteronuclear
boson-boson mixtures loaded in realistic optical lattices, with particular
emphasis on the physics of interfaces. In particular, we numerically reproduce
the recent experimental observation that the addition of a small fraction of K
induces a significant loss of coherence in Rb, providing a simple explanation.
We then investigate the robustness against the inhomogeneity typical of
realistic experimental realizations of the glassy quantum emulsions recently
predicted to occur in strongly interacting boson-boson mixtures on ideal
homogeneous lattices.Comment: 10 pages, 3 figures; some changes in the text and abstract have been
introduced; coherence now given in terms of visibility; a couple of new
reference adde
Design Exploration of an FPGA-Based Multivariate Gaussian Random Number Generator
Monte Carlo simulation is one of the most widely used techniques for computationally
intensive simulations in a variety of applications including mathematical
analysis and modeling and statistical physics. A multivariate Gaussian
random number generator (MVGRNG) is one of the main building blocks of
such a system. Field Programmable Gate Arrays (FPGAs) are gaining increased
popularity as an alternative means to the traditional general purpose
processors targeting the acceleration of the computationally expensive random
number generator block due to their fine grain parallelism and reconfigurability
properties and lower power consumption.
As well as the ability to achieve hardware designs with high throughput it
is also desirable to produce designs with the flexibility to control the resource
usage in order to meet given resource constraints. This work proposes a novel
approach for mapping a MVGRNG onto an FPGA by optimizing the computational
path in terms of hardware resource usage subject to an acceptable
error in the approximation of the distribution of interest. An analysis on the
impact of the error due to truncation/rounding operation along the computational path is performed and an analytical expression of the error inserted into
the system is presented.
Extra dimensionality is added to the feature of the proposed algorithm by
introducing a novel methodology to map many multivariate Gaussian random
number generators onto a single FPGA. The effective resource sharing techniques
introduced in this thesis allows further reduction in hardware resource
usage.
The use of MVGNRG can be found in a wide range of application, especially
in financial applications which involve many correlated assets. In this
work it is demonstrated that the choice of the objective function employed
for the hardware optimization of the MVRNG core has a considerable impact
on the final performance of the application of interest. Two of the most important
financial applications, Value-at-Risk estimation and option pricing are
considered in this work
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