Volatility of exchange rate while changes from time to time, is expected to affect firm level operations as well as aggregate level outcomes i.e. macroeconomic performance. This paper, investigates the effects of exchange rate volatility on aggregate production in Iran using a Structural Vector Auto Regressive model with Exogenous Variables (SVARX). The model is estimated based on macroeconomic...

A SHM method is proposed that minimises the required number of sensors for detecting damage. The damage detection method consists of two steps. In an initial characterization step, substructuring approach is applied to the healthy structure in order to isolate the substructures of interest and later, each substructure is identified by a Vector Auto-Regressive with eXogenous inputs (VARX) model ...

The COVID 19 pandemic has had wide-ranging and severe effects on global economies. Stock markets as usual were the first to react, with drop rates much financial crises of 2008. This study uses daily data model dynamic impact affected countries’ stock market indices commodity markets. panel least squares Vector Auto-Regressive (VAR) estimation results confirm negative short-termed virus spread ...

Abstract. Droughts form a large part of climate- or weather-related disasters reported globally. In Africa, pastoralists living in the arid and semi-arid lands (ASALs) are worse affected. Prolonged dry spells that cause vegetation stress these regions have resulted loss income livelihoods. To curb this, global initiatives like Paris Agreement United Nations recognised need to establish early wa...

A wireless sensing unit is designed for application in structural monitoring and damage detection system. Embedded in the wireless monitoring module, a two-tier prediction model, using auto-regressive (AR) and the autoregressive model with exogenous inputs (ARX) is used for obtaining the damage sensitive feature. To validate the performance of the proposed wireless monitoring and damage detecti...

Vector Auto-regressive (VAR) models are useful for analyzing temporal dependencies among multivariate time series, known as Granger causality. There exist methods for learning sparse VAR models, leading directly to causal networks among the variables of interest. Another useful type of analysis comes from clustering methods, which summarize multiple time series by putting them into groups. We d...

The prediction of visual field deterioration in patients who are suffering from normal tension glaucoma plays an important role in the management of the disease. The Vector Auto-Regressive (VAR) process appears to be an appropriate way of modelling the multivariate time series data from the visual fields. However, standard parameterisation techniques such as the Yule-Walker equations for buildi...

Stochastic, processes can be stationary or nonstationary. They depend on the magnitude of shocks. In other words, in an auto regressive model of order one, the estimated coefficient is not constant. Another finding of this paper is the relation between estimated coefficients and residuals. We also develop a catastrophe and chaos theory for change of roots from stationary to a nonstationary one ...