Sentence examples for forecasting variance from inspiring English sources

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.

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.

This study uses a vector autoregressive (VAR) model with the impulse response function and the forecast variance decomposition error.

The variance decomposition analyses were carried out for a period of 10 years to reveal the forecast variance of the Pakistan Stock Exchange.

This shows that the forecast variance change of international crude oil prices is mainly caused by its own disruptions and the effect is lasting.

In other words, the impact of previous period's forecast variance is more persistence on the stock return's volatility of both indices.

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.

It is noted from the estimated decomposition of forecast variance of the VAR system that stock markets in Austria, Belgium, France, and the Netherlands appear as exogenous in the sense that these markets explain almost 90% of their own fluctuations, leaving approximately only 10% to be explained by others.

The ratio of the excess forecast variance to the minimum forecast variance was 4.016.

However, skilful and reliable predictions may be achieved using a large ensemble to reduce noise and adjusting the forecast variance through a postprocessing technique proposed here.

The reliability-in-the-small was.0000 and the ratio of the excess forecast variance to the minimum forecast variance was 2.526, indicating that the observed variability of the predictions of the Y-ACNAT-NO was approximately 2.5 times the minimum variability that is necessary.