42,141 research outputs found
Response of Firm Agent Network to Exogenous Shock
This paper describes an agent-based model of interacting firms, in which
interacting firm agents rationally invest capital and labor in order to
maximize payoff. Both transactions and production are taken into account in
this model. First, the performance of individual firms on a real transaction
network was simulated. The simulation quantitatively reproduced the cumulative
probability distribution of revenue, material cost, capital, and labor. Then,
the response of the firms to a given exogenous shock, defined as a sudden
change of gross domestic product, is discussed. The longer tail in cumulative
probability and skewed distribution of growth rate are observed for a high
growth scenario.Comment: 8 pages, 9 figures, APFA
Monitoring Networked Applications With Incremental Quantile Estimation
Networked applications have software components that reside on different
computers. Email, for example, has database, processing, and user interface
components that can be distributed across a network and shared by users in
different locations or work groups. End-to-end performance and reliability
metrics describe the software quality experienced by these groups of users,
taking into account all the software components in the pipeline. Each user
produces only some of the data needed to understand the quality of the
application for the group, so group performance metrics are obtained by
combining summary statistics that each end computer periodically (and
automatically) sends to a central server. The group quality metrics usually
focus on medians and tail quantiles rather than on averages. Distributed
quantile estimation is challenging, though, especially when passing large
amounts of data around the network solely to compute quality metrics is
undesirable. This paper describes an Incremental Quantile (IQ) estimation
method that is designed for performance monitoring at arbitrary levels of
network aggregation and time resolution when only a limited amount of data can
be transferred. Applications to both real and simulated data are provided.Comment: This paper commented in: [arXiv:0708.0317], [arXiv:0708.0336],
[arXiv:0708.0338]. Rejoinder in [arXiv:0708.0339]. Published at
http://dx.doi.org/10.1214/088342306000000583 in the Statistical Science
(http://www.imstat.org/sts/) by the Institute of Mathematical Statistics
(http://www.imstat.org
Convolutional Deblurring for Natural Imaging
In this paper, we propose a novel design of image deblurring in the form of
one-shot convolution filtering that can directly convolve with naturally
blurred images for restoration. The problem of optical blurring is a common
disadvantage to many imaging applications that suffer from optical
imperfections. Despite numerous deconvolution methods that blindly estimate
blurring in either inclusive or exclusive forms, they are practically
challenging due to high computational cost and low image reconstruction
quality. Both conditions of high accuracy and high speed are prerequisites for
high-throughput imaging platforms in digital archiving. In such platforms,
deblurring is required after image acquisition before being stored, previewed,
or processed for high-level interpretation. Therefore, on-the-fly correction of
such images is important to avoid possible time delays, mitigate computational
expenses, and increase image perception quality. We bridge this gap by
synthesizing a deconvolution kernel as a linear combination of Finite Impulse
Response (FIR) even-derivative filters that can be directly convolved with
blurry input images to boost the frequency fall-off of the Point Spread
Function (PSF) associated with the optical blur. We employ a Gaussian low-pass
filter to decouple the image denoising problem for image edge deblurring.
Furthermore, we propose a blind approach to estimate the PSF statistics for two
Gaussian and Laplacian models that are common in many imaging pipelines.
Thorough experiments are designed to test and validate the efficiency of the
proposed method using 2054 naturally blurred images across six imaging
applications and seven state-of-the-art deconvolution methods.Comment: 15 pages, for publication in IEEE Transaction Image Processin
Calibration of optimal execution of financial transactions in the presence of transient market impact
Trading large volumes of a financial asset in order driven markets requires
the use of algorithmic execution dividing the volume in many transactions in
order to minimize costs due to market impact. A proper design of an optimal
execution strategy strongly depends on a careful modeling of market impact,
i.e. how the price reacts to trades. In this paper we consider a recently
introduced market impact model (Bouchaud et al., 2004), which has the property
of describing both the volume and the temporal dependence of price change due
to trading. We show how this model can be used to describe price impact also in
aggregated trade time or in real time. We then solve analytically and calibrate
with real data the optimal execution problem both for risk neutral and for risk
averse investors and we derive an efficient frontier of optimal execution. When
we include spread costs the problem must be solved numerically and we show that
the introduction of such costs regularizes the solution.Comment: 31 pages, 8 figure
Money and interest rates under a reserves operating target
This study examines the short-run dynamic relationships between nonborrowed reserves, the federal funds rate, and transaction accounts using daily data from 1979 through 1982. Separate models are estimated for each day of the week, and simulation experiments are performed. The results suggest that the funds rate responded quite rapidly to a change in nonborrowed reserves, but that the short-run nonborrowed reserves multiplier for transaction accounts was only about 18 percent of its theoretical maximum. In addition, the Federal Reserve appeared to accommodate about 65 percent of a permanent shock to money, and lagged reserve requirements seemed to delay depository institutions' response to a money shock.Interest rates ; Bank reserves
Consumer Search on the Internet
This paper uses consumer search data to explain search frictions in online markets, within the context of an equilibrium search model. I use a novel dataset of consumer online browsing and purchasing behavior, which tracks all consumer search prior to each transaction. Using observed search intensities from the online book industry, I estimate search cost distributions that allow for asymmetric consumer sampling. Research on consumer search often assumes a symmetric sampling rule for analytical convenience despite its lack of realism. Search behavior in the online book industry is quite limited: in only 25 percen of the transactions did consumers visit more than one bookstore's website. The industry is characterized by a strong consumer preference for certain retailers. Accounting for unequal consumer sampling halves the search cost estimates from 1.8 to 0.9 dollars per search in the online book industry. Analysis of time spent online suggests substitution between the time consumers spend searching and the relative opportunity cost of their time. Retired people, those with lower education levels, and minorities (with the exception of Hispanics) spent significantly more time searching for a book online. There is a negative relationship between income levels and time spent searching.consumer search, internet, search costs
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