7,728 research outputs found
Do Regional Integration Agreements Increase Business-Cycle Convergence? Evidence From APEC and NAFTA
Using monthly industrial sector data from January 1971 to March 2004, we test for business cycles convergence among the major APEC members: Japan, South Korea, Malaysia, Mexico, USA, and Canada. In addition, we examine the synchronization of business cycles among Australia, Japan, and South Korea, based on the quarterly data for the 1957-2003 period, as well as among the different economic sectors of the NAFTA countries from January 1970 through March 2004. We apply different techniques to identify business cycles. In particular, we propose a new trend-cycle decomposition method based on wavelet analysis. The results show that convergence of business cycles of Asia-Pacific countries is far from complete, but joining the APEC has increased the mean correlation of industrial production cycles of the member economies. On the other hand, although some economic sectors of the NAFTA countries already exhibited some degree of business cycle co-movement even during pre-NAFTA period, the volatility of pair-wise correlation of business cycles declined during NAFTA. In addition, we conclude that, in general, the transmission of business cycles is relatively slow, and, consequently, business cycles appear to be asynchronous.http://deepblue.lib.umich.edu/bitstream/2027.42/40151/3/wp765.pd
Nonlocal Myriad Filters for Cauchy Noise Removal
The contribution of this paper is two-fold. First, we introduce a generalized
myriad filter, which is a method to compute the joint maximum likelihood
estimator of the location and the scale parameter of the Cauchy distribution.
Estimating only the location parameter is known as myriad filter. We propose an
efficient algorithm to compute the generalized myriad filter and prove its
convergence. Special cases of this algorithm result in the classical myriad
filtering, respective an algorithm for estimating only the scale parameter.
Based on an asymptotic analysis, we develop a second, even faster generalized
myriad filtering technique.
Second, we use our new approaches within a nonlocal, fully unsupervised
method to denoise images corrupted by Cauchy noise. Special attention is paid
to the determination of similar patches in noisy images. Numerical examples
demonstrate the excellent performance of our algorithms which have moreover the
advantage to be robust with respect to the parameter choice
Do Regional Integration Agreements Increase Business-Cycle Convergence? Evidence From APEC and NAFTA
Using monthly industrial sector data from January 1971 to March 2004, we test for business cycles convergence among the major APEC members: Japan, South Korea, Malaysia, Mexico, USA, and Canada. In addition, we examine the synchronization of business cycles among Australia, Japan, and South Korea, based on the quarterly data for the 1957-2003 period, as well as among the different economic sectors of the NAFTA countries from January 1970 through March 2004. We apply different techniques to identify business cycles. In particular, we propose a new trend-cycle decomposition method based on wavelet analysis. The results show that convergence of business cycles of Asia-Pacific countries is far from complete, but joining the APEC has increased the mean correlation of industrial production cycles of the member economies. On the other hand, although some economic sectors of the NAFTA countries already exhibited some degree of business cycle co-movement even during pre-NAFTA period, the volatility of pair-wise correlation of business cycles declined during NAFTA. In addition, we conclude that, in general, the transmission of business cycles is relatively slow, and, consequently, business cycles appear to be asynchronous.business-cycles convergence, wavelets, APEC, NAFTA
Sampling-based Motion Planning for Active Multirotor System Identification
This paper reports on an algorithm for planning trajectories that allow a
multirotor micro aerial vehicle (MAV) to quickly identify a set of unknown
parameters. In many problems like self calibration or model parameter
identification some states are only observable under a specific motion. These
motions are often hard to find, especially for inexperienced users. Therefore,
we consider system model identification in an active setting, where the vehicle
autonomously decides what actions to take in order to quickly identify the
model. Our algorithm approximates the belief dynamics of the system around a
candidate trajectory using an extended Kalman filter (EKF). It uses
sampling-based motion planning to explore the space of possible beliefs and
find a maximally informative trajectory within a user-defined budget. We
validate our method in simulation and on a real system showing the feasibility
and repeatability of the proposed approach. Our planner creates trajectories
which reduce model parameter convergence time and uncertainty by a factor of
four.Comment: Published at ICRA 2017. Video available at
https://www.youtube.com/watch?v=xtqrWbgep5
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Review of Unbiased FIR Filters, Smoothers, and Predictors for Polynomial Signals
Extracting an estimate of a slowly varying signal corrupted by noise is a common task. Examples can be found in industrial, scientific and biomedical instrumentation. Depending on the nature of the application the signal estimate is allowed to be a delayed estimate of the original signal or, in the other extreme, no delay is tolerated. These cases are commonly referred to as filtering, prediction, and smoothing depending on the amount of advance or lag between the input data set and the output data set. In this review paper we provide a comprehensive set of design and analysis tools for designing unbiased FIR filters, predictors, and smoothers for slowly varying signals, i.e. signals that can be modeled by low order polynomials. Explicit expressions of parameters needed in practical implementations are given. Real life examples are provided including cases where the method is extended to signals that are piecewise slowly varying. A critical view on recursive implementations of the algorithms is provided
The Atacama Cosmology Telescope: A Measurement of the 600< ell <8000 Cosmic Microwave Background Power Spectrum at 148 GHz
We present a measurement of the angular power spectrum of the cosmic
microwave background (CMB) radiation observed at 148 GHz. The measurement uses
maps with 1.4' angular resolution made with data from the Atacama Cosmology
Telescope (ACT). The observations cover 228 square degrees of the southern sky,
in a 4.2-degree-wide strip centered on declination 53 degrees South. The CMB at
arcminute angular scales is particularly sensitive to the Silk damping scale,
to the Sunyaev-Zel'dovich (SZ) effect from galaxy clusters, and to emission by
radio sources and dusty galaxies. After masking the 108 brightest point sources
in our maps, we estimate the power spectrum between 600 < \ell < 8000 using the
adaptive multi-taper method to minimize spectral leakage and maximize use of
the full data set. Our absolute calibration is based on observations of Uranus.
To verify the calibration and test the fidelity of our map at large angular
scales, we cross-correlate the ACT map to the WMAP map and recover the WMAP
power spectrum from 250 < ell < 1150. The power beyond the Silk damping tail of
the CMB is consistent with models of the emission from point sources. We
quantify the contribution of SZ clusters to the power spectrum by fitting to a
model normalized at sigma8 = 0.8. We constrain the model's amplitude ASZ < 1.63
(95% CL). If interpreted as a measurement of sigma8, this implies sigma8^SZ <
0.86 (95% CL) given our SZ model. A fit of ACT and WMAP five-year data jointly
to a 6-parameter LCDM model plus terms for point sources and the SZ effect is
consistent with these results.Comment: 15 pages, 8 figures. Accepted for publication in Ap
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