Trispectrum versus Bispectrum in Single-Field Inflation

In the standard slow-roll inflationary cosmology, quantum fluctuations in a single field, the inflaton, generate approximately Gaussian primordial density perturbations. At present, the bispectrum and trispectrum of the density perturbations have not been observed and the probability distribution for these perturbations is consistent with Gaussianity. However, Planck satellite data will bring a new level of precision to bear on this issue, and it is possible that evidence for non-Gaussian effects in the primordial distribution will be discovered. One possibility is that a trispectrum will be observed without evidence for a non-zero bispectrum. It is not difficult for this to occur in inflationary models where quantum fluctuations in a field other than the inflaton contribute to the density perturbations. A natural question to ask is whether such an observation would rule out the standard scenarios. We explore this issue and find that it is possible to construct single-field models in which inflaton-generated primordial density perturbations have an observable trispectrum, but a bispectrum that is too small to be observed by the Planck satellite. However, an awkward fine tuning seems to be unavoidable.Comment: 15 pages, 3 figures; journal versio

Time Series Forecasting: The Case for the Single Source of Error State Space

The state space approach to modelling univariate time series is now widely used both in theory and in applications. However, the very richness of the framework means that quite different model formulations are possible, even when they purport to describe the same phenomena. In this paper, we examine the single source of error [SSOE] scheme, which has perfectly correlated error components. We then proceed to compare SSOE to the more common version of the state space models, for which all the error terms are independent; we refer to this as the multiple source of error [MSOE] scheme. As expected, there are many similarities between the MSOE and SSOE schemes, but also some important differences. Both have ARIMA models as their reduced forms, although the mapping is more transparent for SSOE. Further, SSOE does not require a canonical form to complete its specification. An appealing feature of SSOE is that the estimates of the state variables converge in probability to their true values, thereby leading to a formal inferential structure for the ad-hoc exponential smoothing methods for forecasting. The parameter space for SSOE models may be specified to match that of the corresponding ARIMA scheme, or it may be restricted to meaningful sub-spaces, as for MSOE but with somewhat different outcomes. The SSOE formulation enables straightforward extensions to certain classes of non-linear models, including a linear trend with multiplicative seasonals version that underlies the Holt-Winters forecasting method. Conditionally heteroscedastic models may be developed in a similar manner. Finally we note that smoothing and decomposition, two crucial practical issues, may be performed within the SSOE framework.ARIMA, Dynamic Linear Models, Equivalence, Exponential Smoothing, Forecasting, GARCH, Holt's Method, Holt-Winters Method, Kalman Filter, Prediction Intervals.

The structure of Rph, an exoribonuclease from Bacillus anthracis, at 1.7 angstrom resolution

Maturation of tRNA precursors into functional tRNA molecules requires trimming of the primary transcript at both the 5' and 3' ends. Cleavage of nucleotides from the 3' stem of tRNA precursors, releasing nucleotide diphosphates, is accomplished in Bacillus by a phosphate-dependent exoribonuclease, Rph. The crystal structure of this enzyme from B. anthracis has been solved by molecular replacement to a resolution of 1.7 angstrom and refined to an R factor of 19.3%. There is one molecule in the asymmetric unit; the crystal packing reveals the assembly of the protein into a hexamer arranged as a trimer of dimers. The structure shows two sulfate ions bound in the active-site pocket, probably mimicking the phosphate substrate and the phosphate of the 3'-terminal nucleotide of the tRNA precursor. Three other bound sulfate ions point to likely RNA-binding sites

Exploring Privacy Attitudes and Accurate Information Disclosure in Healthcare Contexts

Patients lie to their doctors for a variety of reasons such as judgement, embarrassment, and financial concerns. These reasons are different from consumer contexts where information disclosure risks include the loss of data to unknown third parties. Yet, the same theories are commonly used to frame both settings. This overextension of theory leads to important differences in the results of these studies. We propose that additional theorizing is needed to reconceptualize privacy calculus to more accurately explain healthcare patient disclosure. Using a hybrid methodology based on both a grounded theory approach and construal level theory, this study explores the perceived risks and benefits of health data and consumer data disclosure. The results reveal significant differences in psychological distance and construal levels between the two contexts which can improve future theorizing. This deeper understanding could help improve the accuracy of patient disclosure and reduce harm in clinical systems

Inferring Tax Compliance from Pass-through: Evidence from Airbnb Tax Enforcement Agreements

Tax enforcement is especially costly when market participants are difficult to observe. The benefits of enforcement depend crucially on pre-enforcement compliance. We derive an upper bound on pre-enforcement compliance from the pass-through of newly enforced taxes. Using data on Airbnb listings and the platform’s voluntary collection agreements, we find that taxes are paid on, at most, 24% of Airbnb transactions prior to enforcement. We also find that demand for Airbnb listings is inelastic, driving three key insights: the tax burden falls disproportionately on renters, the excess burden is small, and tax enforcement is relatively ineffective at reducing local Airbnb activity

What Can Mental Health Teach Us About Social Media Screen Time Misestimation?

Mobile platform providers have provided the ability to measure the time consumers spend on each app. This provides the opportunity to measure a consumer’s misestimation of their screen time which is a concept relevant to several mental health attributes such as depression, anxiety, and addiction. We provide additional evidence about the effect of objective screen time on mental health, but add a unique perspective on how screen time misestimation is determined by various mental health attributes. We collected data from a student sample (n=1005) who are from the demographic who most commonly use social media apps (18-29 yr olds). We measured our model across several of the most common platforms including Facebook, Instagram, Twitter, and YouTube to maximize the practical implications. The results indicate that mental health attributes can indeed be reflected by misestimations of screen time. However, this effect varies by social media platform