91 research outputs found
Boosting the Hodrick-Prescott Filter
The Hodrick-Prescott (HP) ļ¬lter is one of the most widely used econometric methods in applied macroeconomic research. The technique is nonparametric and seeks to decompose a time series into a trend and a cyclical component unaided by economic theory or prior trend speciļ¬cation. Like all nonparametric methods, the HP ļ¬lter depends critically on a tuning parameter that controls the degree of smoothing. Yet in contrast to modern nonparametric methods and applied work with these procedures, empirical practice with the HP ļ¬lter almost universally relies on standard settings for the tuning parameter that have been suggested largely by experimentation with macroeconomic data and heuristic reasoning about the form of economic cycles and trends. As recent research has shown, standard settings may not be adequate in removing trends, particularly stochastic trends, in economic data. This paper proposes an easy-to-implement practical procedure of iterating the HP smoother that is intended to make the ļ¬lter a smarter smoothing device for trend estimation and trend elimination. We call this iterated HP technique the boosted HP ļ¬lter in view of its connection to L_2-boosting in machine learning. The paper develops limit theory to show that the boosted HP ļ¬lter asymptotically recovers trend mechanisms that involve unit root processes, deterministic polynomial drifts, and polynomial drifts with structural breaks ā the most common trends that appear in macroeconomic data and current modeling methodology. In doing so, the boosted ļ¬lter provides a new mechanism for consistently estimating multiple structural breaks. A stopping criterion is used to automate the iterative HP algorithm, making it a data-determined method that is ready for modern data-rich environments in economic research. The methodology is illustrated using three real data examples that highlight the diļ¬erences between simple HP ļ¬ltering, the data-determined boosted ļ¬lter, and an alternative autoregressive approach. These examples show that the boosted HP ļ¬lter is helpful in analyzing a large collection of heterogeneous macroeconomic time series that manifest various degrees of persistence, trend behavior, and volatility
Adaptive Sliding Control for a Class of Fractional Commensurate Order Chaotic Systems
This paper proposes adaptive sliding mode control design for a class of fractional commensurate order chaotic systems. We firstly introduce a fractional integral sliding manifold for the nominal systems. Secondly we prove the stability of the corresponding fractional sliding dynamics. Then, by introducing a Lyapunov candidate function and using the Mittag-Leffler stability theory we derive the desired sliding control law. Furthermore, we prove that the proposed sliding manifold is also adapted for the fractional systems in the presence of uncertainties and external disturbances. At last, we design a fractional adaptation law for the perturbed fractional systems. To verify the viability and efficiency of the proposed fractional controllers, numerical simulations of fractional Lorenzās system and Chenās system are presented
Nickell Bias in Panel Local Projection: Financial Crises Are Worse Than You Think
Local Projection is widely used for impulse response estimation, with the
Fixed Effect (FE) estimator being the default for panel data. This paper
highlights the presence of Nickell bias for all regressors in the FE estimator,
even if lagged dependent variables are absent in the regression. This bias is
the consequence of the inherent panel predictive specification. We recommend
using the split-panel jackknife estimator to eliminate the asymptotic bias and
restore the standard statistical inference. Revisiting three macro-finance
studies on the linkage between financial crises and economic contraction, we
find that the FE estimator substantially underestimates the post-crisis
economic losses
Visibility-Aware Pixelwise View Selection for Multi-View Stereo Matching
The performance of PatchMatch-based multi-view stereo algorithms depends
heavily on the source views selected for computing matching costs. Instead of
modeling the visibility of different views, most existing approaches handle
occlusions in an ad-hoc manner. To address this issue, we propose a novel
visibility-guided pixelwise view selection scheme in this paper. It
progressively refines the set of source views to be used for each pixel in the
reference view based on visibility information provided by already validated
solutions. In addition, the Artificial Multi-Bee Colony (AMBC) algorithm is
employed to search for optimal solutions for different pixels in parallel.
Inter-colony communication is performed both within the same image and among
different images. Fitness rewards are added to validated and propagated
solutions, effectively enforcing the smoothness of neighboring pixels and
allowing better handling of textureless areas. Experimental results on the DTU
dataset show our method achieves state-of-the-art performance among
non-learning-based methods and retrieves more details in occluded and
low-textured regions.Comment: 8 page
Demonstration of Geometric Landau-Zener Interferometry in a Superconducting Qubit
Geometric quantum manipulation and Landau-Zener interferometry have been
separately explored in many quantum systems. In this Letter, we combine these
two approaches to study the dynamics of a superconducting phase qubit. We
experimentally demonstrate Landau-Zener interferometry based on the pure
geometric phases in this solid-state qubit. We observe the interference caused
by a pure geometric phase accumulated in the evolution between two consecutive
Landau-Zener transitions, while the dynamical phase is canceled out by a
spin-echo pulse. The full controllability of the qubit state as a function of
the intrinsically robust geometric phase provides a promising approach for
quantum state manipulation.Comment: 5 pages + 3 pages supplemental Materia
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