Forecasting inflation using dynamic model averaging

We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coefficients to change over time, but also allow for the entire forecasting model to change over time. We find that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coefficient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period

Using humanoid robots to study human behavior

Our understanding of human behavior advances as our humanoid robotics work progresses-and vice versa. This team's work focuses on trajectory formation and planning, learning from demonstration, oculomotor control and interactive behaviors. They are programming robotic behavior based on how we humans “program” behavior in-or train-each other

Predicting spectral features in galaxy spectra from broad-band photometry

We explore the prospects of predicting emission line features present in galaxy spectra given broad-band photometry alone. There is a general consent that colours, and spectral features, most notably the 4000 A break, can predict many properties of galaxies, including star formation rates and hence they could infer some of the line properties. We argue that these techniques have great prospects in helping us understand line emission in extragalactic objects and might speed up future galaxy redshift surveys if they are to target emission line objects only. We use two independent methods, Artifical Neural Neworks (based on the ANNz code) and Locally Weighted Regression (LWR), to retrieve correlations present in the colour N-dimensional space and to predict the equivalent widths present in the corresponding spectra. We also investigate how well it is possible to separate galaxies with and without lines from broad band photometry only. We find, unsurprisingly, that recombination lines can be well predicted by galaxy colours. However, among collisional lines some can and some cannot be predicted well from galaxy colours alone, without any further redshift information. We also use our techniques to estimate how much information contained in spectral diagnostic diagrams can be recovered from broad-band photometry alone. We find that it is possible to classify AGN and star formation objects relatively well using colours only. We suggest that this technique could be used to considerably improve redshift surveys such as the upcoming FMOS survey and the planned WFMOS survey.Comment: 10 pages 7 figures summitted to MNRA

Taxing Capital? Not a Bad Idea After All!

Premi a l'excel·lència investigadora. 2010Publicat també com a : CEPR Discussion Paper - ISSN 0265-8003 Núm. 5929 (2006), p. 1-55We quantitatively characterize the optimal capital and labor income tax in an overlapping generations model with idiosyncratic, uninsurable income shocks and permanent productivity differences of households. The optimal capital income tax rate is significantly positive at 36 percent. The optimal progressive labor income tax is, roughly, a flat tax of 23 percent with a deduction of #7,200 (relative to average household income of #42,000). The high optimal capital income tax is mainly driven by the life-cycle structure of the model, whereas the optimal progressivity of the labor income tax is attributable to the insurance and redistribution role of the tax system. (JEL E13, H21, H24, H25

Probabilistic Inference for Fast Learning in Control

We provide a novel framework for very fast model-based reinforcement learning in continuous state and action spaces. The framework requires probabilistic models that explicitly characterize their levels of confidence. Within this framework, we use flexible, non-parametric models to describe the world based on previously collected experience. We demonstrate learning on the cart-pole problem in a setting where we provide very limited prior knowledge about the task. Learning progresses rapidly, and a good policy is found after only a hand-full of iterations

New Barriers to Participation: Application of New Mexico's Voter Identification Law

In democratic societies there is a tension between maximizing ballot access and minimizing voter fraud. Since the 2000 presidential election, this tension has been central to discussions about election reform, at the national and local level. We examine this tension by focusing on the implementation of voter identification laws in one state that has experienced significant issues in recent elections, and that is now implementing significant attempts at election reform: New Mexico. We hypothesized that Hispanic voters were more likely to show some form of identification than other types of voters. Using a voter data set from New Mexico’s First Congressional District in the 2006 election, we find that Hispanic, male and Election Day voters were more likely to show some form of identification than non-Hispanic, female and early voters. In addition, using an overlapping study of Bernalillo County 2006 poll workers, we find no evidence that certain groups of poll workers were more likely to ask for voter identification. Our findings suggest that broad voter identification laws, which may be applied unequally, may be perceived as discriminatory

Optimization And Learning For Rough Terrain Legged Locomotion

We present a novel approach to legged locomotion over rough terrain that is thoroughly rooted in optimization. This approach relies on a hierarchy of fast, anytime algorithms to plan a set of footholds, along with the dynamic body motions required to execute them. Components within the planning framework coordinate to exchange plans, cost-to-go estimates, and \u27certificates\u27 that ensure the output of an abstract high-level planner can be realized by lower layers of the hierarchy. The burden of careful engineering of cost functions to achieve desired performance is substantially mitigated by a simple inverse optimal control technique. Robustness is achieved by real-time re-planning of the full trajectory, augmented by reflexes and feedback control. We demonstrate the successful application of our approach in guiding the LittleDog quadruped robot over a variety of types of rough terrain. Other novel aspects of our past research efforts include a variety of pioneering inverse optimal control techniques as well as a system for planning using arbitrary pre-recorded robot behavior

Searching High Redshift Large-Scale Structures: Photometry of Four Fields Around Quasar Pairs at z~1

We have studied the photometric properties of four fields around the high-redshift quasar pairs QP1310+0007, QP1355-0032, QP0110-0219, and QP0114-3140 at z ~ 1 with the aim of identifying large-scale structures- galaxy clusters or groups- around them. This sample was observed with GMOS in Gemini North and South telescopes in the g', r', i', and z' bands, and our photometry is complete to a limiting magnitude of i' ~ 24 mag (corresponding to ~ M*_i' + 2 at the redshift of the pairs). Our analysis reveals that QP0110-0219 shows very strong and QP1310+0007 and QP1355-0032 show some evidence for the presence of rich galaxy clusters in direct vicinity of the pairs. On the other hand, QP0114-3140 could be an isolated pair in a poor environment. This work suggest that z ~ 1 quasar pairs are excellent tracers of high density environments and this same technique may be useful to find clusters at higher redshifts.Comment: 29 pages, 7 figures, ApJ accepted. Added one figure and 3 references. Some paragraphs was rewritten in sections 1, 3, 5, and 6, as suggested by refere

Enlightening the structure and dynamics of Abell 1942

We present a dynamical analysis of the galaxy cluster Abell 1942 based on a set of 128 velocities obtained at the European Southern Observatory. Data on individual galaxies are presented and the accuracy of the determined velocities is discussed as well as some properties of the cluster. We have also made use of publicly available Chandra X-ray data. We obtained an improved mean redshift value z = 0.22513 \pm 0.0008 and velocity dispersion sigma = 908^{+147}_{-139} km/s. Our analysis indicates that inside a radius of ~1.5 h_{70}^{-1} Mpc (~7 arcmin) the cluster is well relaxed, without any remarkable feature and the X-ray emission traces fairly well the galaxy distribution. Two possible optical substructures are seen at ~5 arcmin from the centre towards the Northwest and the Southwest direction, but are not confirmed by the velocity field. These clumps are however, kinematically bound to the main structure of Abell 1942. X-ray spectroscopic analysis of Chandra data resulted in a temperature kT = 5.5 \pm 0.5 keV and metal abundance Z = 0.33 \pm 0.15 Z_odot. The velocity dispersion corresponding to this temperature using the T_X-sigma scaling relation is in good agreement with the measured galaxies velocities. Our photometric redshift analysis suggests that the weak lensing signal observed at the south of the cluster and previously attributed to a "dark clump", is produced by background sources, possibly distributed as a filamentary structure.Comment: Accepted for publication in Astronomy & Astrophysics, 15 pages, 15 figures, table w/ positions, photometric data and redshift

Deflation and depression: Is there an empirical link?

Are deflation and depression empirically linked? No, concludes a broad historical study of inflation and real output growth rates. Deflation and depression do seem to have been linked during the 1930s. But in the rest of the data for 17 countries and more than 100 years, there is virtually no evidence of such a link