We investigate the dynamic relationship between global oil prices, stock market, and gas (FTSE-OG) returns in UK through a structural vector autoregressive (VAR) framework during COVID-19 pandemic. The VAR results suggest that impact of shocks related to price on FTSE-OG index becomes less important loses its explanatory power However, market increase their variations returns.

‎This paper has two aims. The first is forecasting inflation in Iran using Macroeconomic variables data in Iran (Inflation rate, liquidity, GDP, prices of imported goods and exchange rates) , and the second is comparing the performance of forecasting vector auto regression (VAR), Bayesian Vector-Autoregressive (BVAR), GARCH, time series and neural network models by which Iran's inflation is for...

We propose a testing procedure for assessing the presence of threshold effects in nonstationary vector autoregressive models with or without cointegration. Our approach involves first testing whether the long-run impact matrix characterizing the VECM type representation of the VAR switches according to the magnitude of some threshold variable and is valid regardless of whether the system is pur...

The information contained in one model’s forecast compared to that in another can be assessed from a regression of actual values on predicted values from the hvo models. We do this for forecasts of real GNP growth rates for different pairs of models. The models include a structural model (the Fair (1976) model), uariour versions of the vector autoregressive (VAR) model, and various versions of ...

Estimating the response of hours worked to technology shocks is often considered as a crucial step for evaluating the applicability of macroeconomic models to reality. In particular, Galí [1999] has considered the conditional correlation between employment and productivity as a key tool for building an empirical evaluation of Real Business Cycle theories and New-Keynesian models. Impulse-respon...

I propose a Bayesian approach to identify vector autoregressive (VAR) models via proxies in data-rich environment. The setup augments small-scale VAR model with latent factors . It allows trace out the responses of disaggregated series unified while controlling for broad economic conditions posterior sampler accounts estimation uncertainty these as well measurement precision proxy. In first app...

Purpose The purpose of the research is to assess risk financial market in digital economy through quantitative analysis model big data era. It a challenge for government carry out management Design/methodology/approach In this study, generalized autoregressive conditional heteroskedasticity-vector autoregression (GARCH-VaR) constructed analyze economy. Additionally, correlation test and station...