526 research outputs found

    Do real interest rates converge? Evidence from the European Union

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    We test for real interest parity (RIP) in the EU25 area. Our contribution is two-fold: First, we account for the previously overlooked effects of structural breaks on real interest rate differentials. Second, we test for RIP against the EMU average. For the majority of our sample countries we obtain evidence of real interest rate convergence towards the latter. Convergence, however, is a gradual process subject to structural breaks, typically falling close to the launch of the euro. Our findings have important implications relating to the single monetary policy and the progress new EU members have achieved towards joining the euro.real interest rate parity; convergence, structural breaks; EU; EMU

    Temporally Causal Discovery Tests for Discrete Time Series and Neural Spike Trains

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    We consider the problem of detecting causal relationships between discrete time series, in the presence of potential confounders. A hypothesis test is introduced for identifying the temporally causal influence of (xn)(x_n) on (yn)(y_n), causally conditioned on a possibly confounding third time series (zn)(z_n). Under natural Markovian modeling assumptions, it is shown that the null hypothesis, corresponding to the absence of temporally causal influence, is equivalent to the underlying `causal conditional directed information rate' being equal to zero. The plug-in estimator for this functional is identified with the log-likelihood ratio test statistic for the desired test. This statistic is shown to be asymptotically normal under the alternative hypothesis and asymptotically χ2\chi^2 distributed under the null, facilitating the computation of pp-values when used on empirical data. The effectiveness of the resulting hypothesis test is illustrated on simulated data, validating the underlying theory. The test is also employed in the analysis of spike train data recorded from neurons in the V4 and FEF brain regions of behaving animals during a visual attention task. There, the test results are seen to identify interesting and biologically relevant information.Comment: 31 pages, 4 figure

    Cell-Type-Specific Synchronization of Neural Activity in FEF with V4 during Attention

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    SummaryShifts of gaze and shifts of attention are closely linked and it is debated whether they result from the same neural mechanisms. Both processes involve the frontal eye fields (FEF), an area which is also a source of top-down feedback to area V4 during covert attention. To test the relative contributions of oculomotor and attention-related FEF signals to such feedback, we recorded simultaneously from both areas in a covert attention task and in a saccade task. In the attention task, only visual and visuomovement FEF neurons showed enhanced responses, whereas movement cells were unchanged. Importantly, visual, but not movement or visuomovement cells, showed enhanced gamma frequency synchronization with activity in V4 during attention. Within FEF, beta synchronization was increased for movement cells during attention but was suppressed in the saccade task. These findings support the idea that the attentional modulation of visual processing is not mediated by movement neurons

    Top-Down Control of Visual Attention by the Prefrontal Cortex. Functional Specialization and Long-Range Interactions

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    The ability to select information that is relevant to current behavioral goals is the hallmark of voluntary attention and an essential part of our cognition. Attention tasks are a prime example to study at the neuronal level, how task related information can be selectively processed in the brain while irrelevant information is filtered out. Whereas, numerous studies have focused on elucidating the mechanisms of visual attention at the single neuron and population level in the visual cortices, considerably less work has been devoted to deciphering the distinct contribution of higher-order brain areas, which are known to be critical for the employment of attention. Among these areas, the prefrontal cortex (PFC) has long been considered a source of top-down signals that bias selection in early visual areas in favor of the attended features. Here, we review recent experimental data that support the role of PFC in attention. We examine the existing evidence for functional specialization within PFC and we discuss how long-range interactions between PFC subregions and posterior visual areas may be implemented in the brain and contribute to the attentional modulation of different measures of neural activity in visual cortices

    High-Frequency, Long-Range Coupling Between Prefrontal and Visual Cortex During Attention

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    Electrical recordings in humans and monkeys show attentional enhancement of evoked responses and gamma synchrony in ventral stream cortical areas. Does this synchrony result from intrinsic activity in visual cortex or from inputs from other structures? Using paired recordings in the frontal eye field (FEF) and area V4, we found that attention to a stimulus in their joint receptive field leads to enhanced oscillatory coupling between the two areas, particularly at gamma frequencies. This coupling appeared to be initiated by FEF and was time-shifted by about 8 to 13 milliseconds across a range of frequencies. Considering the expected conduction and synaptic delays between the areas, this time-shifted coupling at gamma frequencies may optimize the postsynaptic impact of spikes from one area upon the other, improving cross-area communication with attention.Grant EY017292Grant EY1792

    The usefulness of econometric models with stochastic volatility and long memory : applications for macroeconomic and financial time series

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    This study aims to examine the usefulness of econometric models with stochastic volatility and long memory in the application of macroeconomic and financial time series. An ARFIMA-FIAPARCH process is used to estimate the two main parameters driving the degree of persistence in the US real interest rate and its uncertainty. It provides evidence that the US real interest rates exhibit dual long memory and suggests that much more attention needs to be paid to the degree of persistence and its consequences for the economic theories which are still inconsistent with the finding of either near-unit-root or long memory mean-reverting behavior. A bivariate GARCH-type of model with/without long-memory is constructed to concern the issue of temporal ordering of inflation, output growth and their respective uncertainties as well as all the possible causal relationships among the four variables in the US/UK, allowing several lags of the conditional variances/levels used as regressors in the mean/variance equations. Notably, the findings are quite robust to changes in the specification of the model. The applicability and out-of-sample forecasting ability of a multivariate constant conditional correlation FIAPARCH model are analysed through a multi-country study of national stock market returns. This multivariate specification is generally applicable once power, leverage and long-memory effects are taken into consideration. In addition, both the optimal fractional differencing parameter and power transformation are remarkably similar across countries.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Univariate and multivariate GARCH models applied to the CARBS indices

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    Abstract: The purpose of this paper is to estimate the calibrated parameters of different univariate and multivariate GARCH family models. It is unrealistic to assume that volatility of financial returns is constant. In the empirical analysis, the symmetric GARCH, and asymmetric GJR-GARCH and EGARCH models were estimated for the CARBS indices and a global minimum variance portfolio (GMVP), the best fitting model was determined using the AIC and BIC. The asymmetric terms of the GJR-GARCH and EGARCH models indicate signs of the leverage effect. The information criterion suggest that the EGARCH model is the best fitting model for the CARBS indices and the GMVP
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