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This study uses a vector autoregressive (VAR) model with the impulse response function and the forecast variance decomposition error.
This shows that the forecast variance change of international crude oil prices is mainly caused by its own disruptions and the effect is lasting.
The variance decomposition analyses were carried out for a period of 10 years to reveal the forecast variance of the Pakistan Stock Exchange.
In other words, the impact of previous period's forecast variance is more persistence on the stock return's volatility of both indices.
Extensive Monte Carlo simulations, using dynamic factor models, clarify the relationship between the achieved reduction in forecast variance and various design parameters, such as the observation length, the number of predictors, and the length of the forecast horizon.
Furthermore, we applied a variance decomposition test to understand the forecast variance and impulse response function in order to analyze the effect of the oil price shock on the financial market.
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Since the estimated coefficients from VAR models often appear to lack statistical significance due to the inaccurate estimates of standard errors, researchers often use impulse response functions (IRFs) and forecasting variance decomposition to explain the dynamic effects of the shocks on the endogenous variables.
As explained in the Methods section, the forecast error variance of each stock market of the region is allocated to provide the component of forecast error variance that is for by innovations in each of the seventeen stock markets.
GARCH model is frequently used to forecast the variance of time series.
Targeting measurements based on the decrease in forecast error variance was shown to be more efficient than a systematic sampling method.
Next to Austria, Belgium, France, and the Netherlands, in terms of exogeneity, comes Britain and Greece, which explain almost 70 to 100% of forecast error variance by themselves.
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