The Causality and Economic Impact of FDI inflows from Trade Partners in Pakistan

This paper examines causality between FDI, GDP, Exports and Domestic Investment by using Granger and multivariate Granger causality tests. The study also employs gravity based panel model to investigate the impact of FDI inflows from trade partners on GDP, trade and domestic investment in Pakistan. The results show that two-way causality runs between GDP, domestic investment and FDI, while unidirectional causality is detected from exports to FDI. Our panel data estimation confirms the positive role of FDI inflows in GDP and domestic investment while the results shows that the role of FDI is insignificant in case of exports and imports. Similarly, the concentration and sporadic FDI inflows from a few trade partners is adversely affecting GDP and increases imports without affecting domestic investment and exports. On the other hand minor FDI inflows from trade partners significantly contribute to GDP and decreases imports.trade partners, causality, gravity model, concentration

Lagged and instantaneous dynamical influences related to brain structural connectivity

Contemporary neuroimaging methods can shed light on the basis of human neural and cognitive specializations, with important implications for neuroscience and medicine. Different MRI acquisitions provide different brain networks at the macroscale; whilst diffusion-weighted MRI (dMRI) provides a structural connectivity (SC) coincident with the bundles of parallel fibers between brain areas, functional MRI (fMRI) accounts for the variations in the blood-oxygenation-level-dependent T2* signal, providing functional connectivity (FC).Understanding the precise relation between FC and SC, that is, between brain dynamics and structure, is still a challenge for neuroscience. To investigate this problem, we acquired data at rest and built the corresponding SC (with matrix elements corresponding to the fiber number between brain areas) to be compared with FC connectivity matrices obtained by 3 different methods: directed dependencies by an exploratory version of structural equation modeling (eSEM), linear correlations (C) and partial correlations (PC). We also considered the possibility of using lagged correlations in time series; so, we compared a lagged version of eSEM and Granger causality (GC). Our results were two-fold: firstly, eSEM performance in correlating with SC was comparable to those obtained from C and PC, but eSEM (not C nor PC) provides information about directionality of the functional interactions. Second, interactions on a time scale much smaller than the sampling time, captured by instantaneous connectivity methods, are much more related to SC than slow directed influences captured by the lagged analysis. Indeed the performance in correlating with SC was much worse for GC and for the lagged version of eSEM. We expect these results to supply further insights to the interplay between SC and functional patterns, an important issue in the study of brain physiology and function.Comment: Accepted and published in Frontiers in Psychology in its current form. 27 pages, 1 table, 5 figures, 2 suppl. figure

Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena

Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is further complicated by many theoretical issues, such as the I-equivalence among different structures. In this work, we focus on a specific subclass of BNs, named Suppes-Bayes Causal Networks (SBCNs), which include specific structural constraints based on Suppes' probabilistic causation to efficiently model cumulative phenomena. Here we compare the performance, via extensive simulations, of various state-of-the-art search strategies, such as local search techniques and Genetic Algorithms, as well as of distinct regularization methods. The assessment is performed on a large number of simulated datasets from topologies with distinct levels of complexity, various sample size and different rates of errors in the data. Among the main results, we show that the introduction of Suppes' constraints dramatically improve the inference accuracy, by reducing the solution space and providing a temporal ordering on the variables. We also report on trade-offs among different search techniques that can be efficiently employed in distinct experimental settings. This manuscript is an extended version of the paper "Structural Learning of Probabilistic Graphical Models of Cumulative Phenomena" presented at the 2018 International Conference on Computational Science

BALANCING THE BUDGET THROUGH REVENUE OR SPENDING ADJUSTMENTS? THE CASE OF GREECE

This paper examines the solvency of the Greek fiscal policy. Employing a cointegrated VAR as a benchmark, evidence of a long-run link between revenues and spending is presented, although intertemporal solvency is violated. Utilizing Granger-causality tests, a test for fiscal adjustment neutrality and Generalized Impulse Responses, this paper provides evidence in favor of the ¡®tax and spend¡¯ hypothesis for Greece. Additionally, the empirical evidence indicates that fiscal adjustment should take place through spending rather than revenue adjustment.Budget Balance, Government Revenue and Spending, Causality, Generalized Impulse Responses, Greece

Advertising, Consumption and Economic Growth: An Empirical Investigation

It is sometimes argued that more advertising raises consumption which in turn stimulates output and so economic growth. We test this hypothesis using annual German data expressed in terms of GDP for the period 1950-2000. We find that advertising does not Granger-cause growth but Granger-causes consumption. Consumption, in turn, Granger-causes GDP growth. The data imply that the immediate impact of more advertising on consumption is positive. However, the long-run effect is negative. Further- more, the immediate impact of higher consumption on growth is negative. But the long-run effect is positive. These results raise interesting questions for standard theory, political debates and advertising practioners.Advertising, Consumption, Economic Growth

DYNAMICS OF REGIONAL FED CATTLE PRICES

The dynamic relationship between four regional cash prices for fed (slaughter) cattle is investigated using time series analysis and causality tests. The results indicate that price adjustments to new information take about one week. Texas Panhandle price also was determined to dominate the price discovery process. Regional prices also were found to be interdependent. This suggests that increasing regional meat packer concentration may not grant meat packers increased regional market power in their pricing practices.Demand and Price Analysis, Livestock Production/Industries,

Regime-switching Vector Error Correction Model (VECM) analysis of UK meat consumption

The asymptotic distributions of cointegration tests are approximated using the Gamma distribution. The tests considered are for the I(1), the conditional I(1), as well as the I(2) model. Formulae for the parameters of the Gamma distributions are derived from response surfaces. The resulting approximation is flexible, easy to implement and more accurate than the standard tables previously published

EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks.

Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a "reach/saccade to spatial target" cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI