Last Night a Shrinkage Saved My Life: Economic Growth,Model Uncertainty and Correlated Regressors

We compare the predictive ability of Bayesian methods which deal simultaneously with model uncertainty and correlated regressors in the framework of cross‐country growth regressions. In particular, we assess methods with spike and slab priors combined with different prior specifications for the slope parameters in the slab. Our results indicate that moving away from Gaussian g‐priors towards Bayesian ridge, LASSO or elastic net specifications has clear advantages for prediction when dealing with datasets of (potentially highly) correlated regressors, a pervasive characteristic of the data used hitherto in the econometric literature. Copyright © 2015 John Wiley & Sons, Ltd.     

First record of Monobrachium parasitum Mereschkowsky, 1877 (Cnidaria: Hydrozoa) from Brazil,in the tropical southwestern Atlantic,and its implication for bipolarity concepts

Project HABITATS was recently initiated in the Campos Basin (20.5–24° S), state of Rio de Janeiro, to assess biological impacts of petroleum exploration and exploitation on the continental shelf and slope of Brazil. Among species discovered in benthic samples from the area is the hydroid Monobrachium parasitum Mereschkowsky, 1877, occurring in an epibiotic association with the pelecypod mollusc Mendicula ferruginosa (Forbes, 1844): this is the first report of an association between the two species. Monobrachium parasitum has always been reported from cold waters, and is generally considered bipolar. This report records the occurrence of M. parasitum at lower latitudes, where they have seldom been collected: it is also the first account of this species from South America and the southwestern Atlantic. Moreover, the bathymetric distribution of M. parasitum is extended to a depth of 998 m. No previous records exist of hydroids from the Brazilian continental slope.     

Is forecasting with large models informative? Assessing the role of judgement in macroeconomic forecasts

We evaluate residual projection strategies in the context of a large‐scale macro model of the euro area and smaller benchmark time‐series models. The exercises attempt to measure the accuracy of model‐based forecasts simulated both out‐of‐sample and in‐sample. Both exercises incorporate alternative residual‐projection methods, to assess the importance of unaccounted‐for breaks in forecast accuracy and off‐model judgement. Conclusions reached are that simple mechanical residual adjustments have a significant impact on forecasting accuracy irrespective of the model in use, likely due to the presence of breaks in trends in the data. The testing procedure and conclusions are applicable to a wide class of models and of general interest. Copyright © 2010 John Wiley & Sons, Ltd.     

Introducing System Identification Strategy into Model Predictive Control

As system identification theory and model predictive control are belonged to two different research fields separately, so one gap exists between these two subjects. To alleviate this gap between them, one new idea proposed in this paper is to introduce system identification theory into model predictive control. As the most important element in model predictive control is the prediction of the output value for a nonlinear system, then the problem of deriving the prediction of the output value can be achieved by system identification theory. More specifically, a Bayesian approach is applied for the nonparametric estimation by modeling the prediction as realizations of zero mean random fields.Through comparing this kind of prediction corresponding to this Bayesian approach and the former direct weight optimization identification for nonlinear system, the authors see that if the unknown weights are chosen appropriately, these two approaches are equivalent to each other. Based on the obtained prediction of the output value, the authors substitute this prediction of the output value into one cost function of model predictive control, and then a quadratic programming problem with inequality constraints is formulated. When to solve this quadratic programming problem, a detailed process about how to derive its dual form is given. As the dual problem has a simple constraint set, it is amenable to the use of the common Gauss-Seidel algorithm, whose convergence can be shown easily.Finally, one simulation example confirms the proposed theoretical results.     

Experience in combining subjective and quantitative forecasts of open market rates

This paper presents a method of combining subjective information from open-market operators with results from a time-series forecasting model. Empirical results of forecasts for interest rates of bank reserves are presented.     

Beating the random walk in Central and Eastern Europe

We compare the accuracy of vector autoregressive (VAR), restricted vector autoregressive (RVAR), Bayesian vector autoregressive (BVAR), vector error correction (VEC) and Bayesian error correction (BVEC) models in forecasting the exchange rates of five Central and Eastern European currencies (Czech Koruna, Hungarian Forint, Slovak Koruna, Slovenian Tolar and Polish Zloty) against the US Dollar and the Euro. Although these models tend to outperform the random walk model for long‐term predictions (6 months ahead and beyond), even the best models in terms of average prediction error fail to reject the test of equality of forecasting accuracy against the random walk model in short‐term predictions. Copyright © 2005 John Wiley & Sons, Ltd.     

Using induced pluripotent stem cells to investigate cardiac phenotypes in Timothy syndrome

Individuals with congenital or acquired prolongation of the QT interval, or long QT syndrome (LQTS), are at risk of life-threatening ventricular arrhythmia. LQTS is commonly genetic in origin but can also be caused or exacerbated by environmental factors. A missense mutation in the L-type calcium channel Ca(V)1.2 leads to LQTS in patients with Timothy syndrome. To explore the effect of the Timothy syndrome mutation on the electrical activity and contraction of human cardiomyocytes, we reprogrammed human skin cells from Timothy syndrome patients to generate induced pluripotent stem cells, and differentiated these cells into cardiomyocytes. Electrophysiological recording and calcium (Ca(2+)) imaging studies of these cells revealed irregular contraction, excess Ca(2+) influx, prolonged action potentials, irregular electrical activity and abnormal calcium transients in ventricular-like cells. We found that roscovitine, a compound that increases the voltage-dependent inactivation of Ca(V)1.2 (refs 6-8), restored the electrical and Ca(2+) signalling properties of cardiomyocytes from Timothy syndrome patients. This study provides new opportunities for studying the molecular and cellular mechanisms of cardiac arrhythmias in humans, and provides a robust assay for developing new drugs to treat these diseases.     

Modelling conservation in the Amazon basin

Expansion of the cattle and soy industries in the Amazon basin has increased deforestation rates and will soon push all-weather highways into the region's core. In the face of this growing pressure, a comprehensive conservation strategy for the Amazon basin should protect its watersheds, the full range of species and ecosystem diversity, and the stability of regional climates. Here we report that protected areas in the Amazon basin--the central feature of prevailing conservation approaches--are an important but insufficient component of this strategy, based on policy-sensitive simulations of future deforestation. By 2050, current trends in agricultural expansion will eliminate a total of 40% of Amazon forests, including at least two-thirds of the forest cover of six major watersheds and 12 ecoregions, releasing 32 +/- 8 Pg of carbon to the atmosphere. One-quarter of the 382 mammalian species examined will lose more than 40% of the forest within their Amazon ranges. Although an expanded and enforced network of protected areas could avoid as much as one-third of this projected forest loss, conservation on private lands is also essential. Expanding market pressures for sound land management and prevention of forest clearing on lands unsuitable for agriculture are critical ingredients of a strategy for comprehensive conservation.     

Estimation and Forecasting of Locally Stationary Processes

This paper develops a state space framework for the statistical analysis of a class of locally stationary processes. The proposed Kalman filter approach provides a numerically efficient methodology for estimating and predicting locally stationary models and allows for the handling of missing values. It provides both exact and approximate maximum likelihood estimates. Furthermore, as suggested by the Monte Carlo simulations reported in this work, the performance of the proposed methodology is very good, even for relatively small sample sizes. Copyright © 2011 John Wiley & Sons, Ltd.