Parameter misspecification and robust monetary policy rules

In this paper, I evaluate the performance deterioration that occurs when the central bank employs an optimal targeting rule that is based on incorrect parameter values. I focus on two parameters — the degree of inflation inertia and the degree of price stickiness. I explicitly account for the effects of the structural parameters on the objective function used to evaluate outcomes, as well as on the model’s behavioral equations. The costs of using simple rules relative to the costs of parameter misspecification are also assessed. JEL Classification: E52, E58Misspecification, monetary policy, Robustness

Visual slant misperception and the Black-Hole landing situation

A theory which explains the tendency for dangerously low approaches during night landing situations is presented. The two dimensional information at the pilot's eye contains sufficient information for the visual system to extract the angle of slant of the runway relative to the approach path. The analysis is depends upon perspective information which is available at a certain distance out from the aimpoint, to either side of the runway edgelights. Under black hole landing conditions, however, this information is not available, and it is proposed that the visual system use instead the only available information, the perspective gradient of the runway edgelights. An equation is developed which predicts the perceived approach angle when this incorrect parameter is used. The predictions are in close agreement with existing experimental data

Maximum approximate entropy and r threshold: A new approach for regularity changes detection

Approximate entropy (ApEn) has been widely used as an estimator of regularity in many scientific fields. It has proved to be a useful tool because of its ability to distinguish different system's dynamics when there is only available short-length noisy data. Incorrect parameter selection (embedding dimension $m$, threshold $r$ and data length $N$) and the presence of noise in the signal can undermine the ApEn discrimination capacity. In this work we show that $r_{max}$ ($ApEn(m,r_{max},N)=ApEn_{max}$) can also be used as a feature to discern between dynamics. Moreover, the combined use of $ApEn_{max}$ and $r_{max}$ allows a better discrimination capacity to be accomplished, even in the presence of noise. We conducted our studies using real physiological time series and simulated signals corresponding to both low- and high-dimensional systems. When $ApEn_{max}$ is incapable of discerning between different dynamics because of the noise presence, our results suggest that $r_{max}$ provides additional information that can be useful for classification purposes. Based on cross-validation tests, we conclude that, for short length noisy signals, the joint use of $ApEn_{max}$ and $r_{max}$ can significantly decrease the misclassification rate of a linear classifier in comparison with their isolated use

Noise and howling mitigation in audio/video conferencing

Loudspeaker-to-microphone feedback causes echoes in audio/video conferences. A microphone typically has a background-noise level estimator, which provides noise-level estimates for the purpose of setting parameters within acoustic echo cancelers (AEC), noise reducers, comfort noise generators (CNG), etc. Output from a noisy loudspeaker may be picked up by the microphone, adding to background noise already present at the microphone. Loudspeaker noise, when picked up by the microphone, causes a misestimation of background noise, which in turn leads to incorrect parameter setting. This can lead to divergence of adaptive AEC circuitry, audible echoes, howling, etc

An MCMC Fitting Method for Triaxial Dark Matter Haloes

Measuring the 3D distribution of mass on galaxy cluster scales is a crucial test of the LCDM model, providing constraints on the behaviour of dark matter. Recent work investigating mass distributions of individual galaxy clusters (e.g. Abell 1689) using weak and strong gravitational lensing has revealed potential inconsistencies between the predictions of structure formation models relating halo mass to concentration and those relationships as measured in massive clusters. However, such analyses employ simple spherical halo models while a growing body of work indicates that triaxial 3D halo structure is both common and important in parameter estimates. The very strong assumptions about the symmetry of the lensing halo implied with circular or perturbative elliptical NFW models are not physically motivated and lead to incorrect parameter estimates with significantly underestimated error bars. We here introduce a Markov Chain Monte Carlo (MCMC) method to fit fully triaxial models to weak lensing data that gives parameter and error estimates that fully incorporate the true uncertainty present in nature. Applying the MCMC triaxial fitting method to a population of NFW triaxial lenses drawn from the shape distribution of structure formation simulations, we find that including triaxiality cannot explain a population of massive, highly concentrated clusters within the framework of LCDM, but easily explains rare cases of apparently massive, highly concentrated, very efficient lensing clusters. Our MCMC triaxial NFW fitting method is easily expandable to include constraints from additional data types, and its application returns model parameters and errors that more accurately capture the true (and limited) extent of our knowledge of the structure of galaxy cluster lenses. (abridged)Comment: 18 pages, 15 figures. Updated to match published versio

Cosmology with the largest galaxy cluster surveys: Going beyond Fisher matrix forecasts

We make the first detailed MCMC likelihood study of cosmological constraints that are expected from some of the largest, ongoing and proposed, cluster surveys in different wave-bands and compare the estimates to the prevalent Fisher matrix forecasts. Mock catalogs of cluster counts expected from the surveys -- eROSITA, WFXT, RCS2, DES and Planck, along with a mock dataset of follow-up mass calibrations are analyzed for this purpose. A fair agreement between MCMC and Fisher results is found only in the case of minimal models. However, for many cases, the marginalized constraints obtained from Fisher and MCMC methods can differ by factors of 30-100%. The discrepancy can be alarmingly large for a time dependent dark energy equation of state, w(a); the Fisher methods are seen to under-estimate the constraints by as much as a factor of 4--5. Typically, Fisher estimates become more and more inappropriate as we move away from LCDM, to a constant-w dark energy to varying-w dark energy cosmologies. Fisher analysis, also, predicts incorrect parameter degeneracies. From the point of mass-calibration uncertainties, a high value of unknown scatter about the mean mass-observable relation, and its redshift dependence, is seen to have large degeneracies with the cosmological parameters sigma_8 and w(a) and can degrade the cosmological constraints considerably. We find that the addition of mass-calibrated cluster datasets can improve dark energy and sigma_8 constraints by factors of 2--3 from what can be obtained compared to CMB+SNe+BAO only. Since, details of future cluster surveys are still being planned, we emphasize that optimal survey design must be done using MCMC analysis rather than Fisher forecasting. [abridged]Comment: 26 pages, 13 figures, 7 tables, accepted for publication in JCA

Evaluation of a load cell model for dynamic calibration of the rotor systems research aircraft

The Rotor Systems Research Aircraft uses load cells to isolate the rotor/transmission system from the fuselage. An analytical model of the relationship between applied rotor loads and the resulting load cell measurements is derived by applying a force-and-moment balance to the isolated rotor/transmission system. The model is then used to estimate the applied loads from measured load cell data, as obtained from a ground-based shake test. Using nominal design values for the parameters, the estimation errors, for the case of lateral forcing, were shown to be on the order of the sensor measurement noise in all but the roll axis. An unmodeled external load appears to be the source of the error in this axis