Lack of long-term cortical reorganization after macaque retinal lesions

Several aspects of cortical organization are thought to remain plastic into adulthood, allowing cortical sensorimotor maps to be modified continuously by experience. This dynamic nature of cortical circuitry is important for learning, as well as for repair after injury to the nervous system. Electrophysiology studies suggest that adult macaque primary visual cortex (V1) undergoes large-scale reorganization within a few months after retinal lesioning, but this issue has not been conclusively settled. Here we applied the technique of functional magnetic resonance imaging (fMRI) to detect changes in the cortical topography of macaque area V1 after binocular retinal lesions. fMRI allows non-invasive, in vivo, long-term monitoring of cortical activity with a wide field of view, sampling signals from multiple neurons per unit cortical area. We show that, in contrast with previous studies, adult macaque V1 does not approach normal responsivity during 7.5 months of follow-up after retinal lesions, and its topography does not change. Electrophysiology experiments corroborated the fMRI results. This indicates that adult macaque V1 has limited potential for reorganization in the months following retinal injury.     

On the predictability of crude oil market: A hybrid multiscale wavelet approach

Past research indicates that forecasting is important in understanding price dynamics across assets. We explore the potentiality of multiscale forecasting in the crude oil market by employing a wavelet multiscale analysis on returns and volatilities of Brent and West Texas Intermediate crude oil indices between January 1, 2001, and May 1, 2015. The analysis is based on a shift-invariant discrete wavelet transform, augmented by an entropy-based methodology for determining the optimal timescale decomposition under different market regimes. The empirical results show that the five-step-ahead wavelet forecast that is based on volatilities outperforms the random walk forecast, relative to the wavelet forecast that is based on returns. Optimal wavelet causality forecasting for returns is suggested across all frequencies (i.e., daily–yearly), whereas for volatilities it is suggested only up to quarterly frequencies. These results may have important implications for market efficiency and predictability of prices on the crude oil markets.     

Direction‐of‐change forecasting using a volatility‐based recurrent neural network

This paper investigates the profitability of a trading strategy, based on recurrent neural networks, that attempts to predict the direction‐of‐change of the market in the case of the NASDAQ composite index. The sample extends over the period 8 February 1971 to 7 April 1998, while the sub‐period 8 April 1998 to 5 February 2002 has been reserved for out‐of‐sample testing purposes. We demonstrate that the incorporation in the trading rule of estimates of the conditional volatility changes strongly enhances its profitability, after the inclusion of transaction costs, during bear market periods. This improvement is being measured with respect to a nested model that does not include the volatility variable as well as to a buy‐and‐hold strategy. We suggest that our findings can be justified by invoking either the ‘volatility feedback’ theory or the existence of portfolio insurance schemes in the equity markets. Our results are also consistent with the view that volatility dependence produces sign dependence. Copyright © 2008 John Wiley & Sons, Ltd.     

Nucleosynthetic signatures of the first stars

The chemically most primitive stars provide constraints on the nature of the first stellar objects that formed in the Universe; elements other than hydrogen, helium and traces of lithium present within these objects were generated by nucleosynthesis in the very first stars. The relative abundances of elements in the surviving primitive stars reflect the masses of the first stars, because the pathways of nucleosynthesis are quite sensitive to stellar masses. Several models have been suggested to explain the origin of the abundance pattern of the giant star HE0107-5240, which hitherto exhibited the highest deficiency of heavy elements known. Here we report the discovery of HE1327-2326, a subgiant or main-sequence star with an iron abundance about a factor of two lower than that of HE0107-5240. Both stars show extreme overabundances of carbon and nitrogen with respect to iron, suggesting a similar origin of the abundance patterns. The unexpectedly low Li and high Sr abundances of HE1327-2326, however, challenge existing theoretical understanding: no model predicts the high Sr abundance or provides a Li depletion mechanism consistent with data available for the most metal-poor stars.     

Forecasting US GNP growth: The role of uncertainty

A large number of models have been developed in the literature to analyze and forecast changes in output dynamics. The objective of this paper was to compare the predictive ability of univariate and bivariate models, in terms of forecasting US gross national product (GNP) growth at different forecasting horizons, with the bivariate models containing information on a measure of economic uncertainty. Based on point and density forecast accuracy measures, as well as on equal predictive ability (EPA) and superior predictive ability (SPA) tests, we evaluate the relative forecasting performance of different model specifications over the quarterly period of 1919:Q2 until 2014:Q4. We find that the economic policy uncertainty (EPU) index should improve the accuracy of US GNP growth forecasts in bivariate models. We also find that the EPU exhibits similar forecasting ability to the term spread and outperforms other uncertainty measures such as the volatility index and geopolitical risk in predicting US recessions. While the Markov switching time‐varying parameter vector autoregressive model yields the lowest values for the root mean squared error in most cases, we observe relatively low values for the log predictive density score, when using the Bayesian vector regression model with stochastic volatility. More importantly, our results highlight the importance of uncertainty in forecasting US GNP growth rates.     

Policy‐Oriented Macroeconomic Forecasting with Hybrid DGSE and Time‐Varying Parameter VAR Models

Micro‐founded dynamic stochastic general equilibrium (DSGE) models appear to be particularly suited to evaluating the consequences of alternative macroeconomic policies. Recently, increasing efforts have been undertaken by policymakers to use these models for forecasting, although this proved to be problematic due to estimation and identification issues. Hybrid DSGE models have become popular for dealing with some of the model misspecifications and the trade‐off between theoretical coherence and empirical fit, thus allowing them to compete in terms of predictability with VAR models. However, DSGE and VAR models are still linear and they do not consider time variation in parameters that could account for inherent nonlinearities and capture the adaptive underlying structure of the economy in a robust manner. This study conducts a comparative evaluation of the out‐of‐sample predictive performance of many different specifications of DSGE models and various classes of VAR models, using datasets for the real GDP, the harmonized CPI and the nominal short‐term interest rate series in the euro area. Simple and hybrid DSGE models were implemented, including DSGE‐VAR and factor‐augmented DGSE, and tested against standard, Bayesian and factor‐augmented VARs. Moreover, a new state‐space time‐varying VAR model is presented. The total period spanned from 1970:Q1 to 2010:Q4 with an out‐of‐sample testing period of 2006:Q1–2010:Q4, which covers the global financial crisis and the EU debt crisis. The results of this study can be useful in conducting monetary policy analysis and macro‐forecasting in the euro area. Copyright © 2016 John Wiley & Sons, Ltd.