A dynamic Boltzmann machine (DyBM) has been proposed as a model of a spiking neural network, and its learning rule of maximizing the log-likelihood of given time-series has been shown to exhibit key properties of spike-timing dependent plasticity (STDP), which had been postulated and experimentally confirmed in the field of neuroscience as a learning rule that refines the Hebbian rule. Here, we...

In this paper we present a semi-automated search procedure to deal with the problem of the identification of the contemporaneous causal structure connected to a large class of multivariate time series models. We refer in particular to multivariate models, such as vector autoregressive (VAR) and dynamic factor (DF) model, in which the background or theoretical knowledge is not sufficient or enou...

This paper examines the relationship between current account (CA) and foreign direct investment (FDI) net inflow in Southeast Europe (SEE) countries. The panel data framework of five SEE countries for period 2000- 2020 are used. Our research has three main findings. First, using vector autoregressive VAR(2) model, a long-run CA FDI is identified (a 1% increase leads to 1.011% deficit). suggests...

The exposure rate to air pollution in most urban cities is really a major concern because it results life-threatening consequence for human health and wellbeing. Furthermore, the accurate estimation continuous forecasting of levels very complicated task. In this paper, one space-temporal models, vector autoregressive (VAR) with neural network (NN) genetic algorithm (GA) was proposed enhanced. V...

The U.S. shale revolution, using new technologies to extract crude oil, has led dynamics in the supply side of global oil market. We ask whether revolution dampened role geopolitical risk price volatility. extend a reduced form Structural Break Threshold Vector Autoregressive (SBT-VAR) model structural SBT-VAR and identify innovations by allowing conditional heteroskedasticity. Compared with co...

Abstract Up to now, Generalized Space-Time Autoregressive (GSTAR) models are focused only for univariate spatial-temporal data. This research proposes an extension of GSTAR multivariate data, known as Multivariate or MGSTAR. Three studies were conducted in this research, i.e., theoretical, simulation, and applied studies. These initially developed based on bivariate A theoretical study was done...

The present work aimed to examine the association between Corporate Social performance (CSP) and corporate financial (CFP) taking into account social irresponsibility. Here, we used a sample of French non-financial firms listed on SBF 120 2011 2016. Our findings provided evidence that responsibility (CSR) irresponsibility (CSI) exert opposite effects CFP. Using an estimation vector autoregressi...

The main objective of this study is to examine the effect sickle energy consumption, renewable energy, and forest area on emission carbon dioxide (CO2) in Morocco. Many studies have abord subject using a different approachs, most which used econometric models such as Vector Autoregressive (VAR) Error Correction Model (ECM) Distributed Lag (ARDL). In study, we opted for Non-linear (NARDL) model....