This study uses several econometric techniques for the analyses, namely, the Johansen-Juselius cointegration test, generalized autoregressive conditional heteroskedasticity (GARCH) model, exponential generalized autoregressive conditional heteroskedasticity (EGARCH) model, variance decomposition method, and impulse response function.
Furthermore, compared with the autoregressive integrated moving average model, the decomposition method is recommended in the analysis and prediction of groundwater levels.
Both ARIMA model and decomposition method could meet the forecast accuracy, but ARIMA could not show the characteristics of groundwater level fluctuation (such as periodicity); so, the decomposition method is recommended for the analysis of the dynamic characteristics of groundwater level.
To enhance our understanding of the dynamic characteristics of groundwater level in the western Jilin Province of China, two models of decomposition method in time series analysis, additive model and multiplicative model, are employed in this study.
Many previous research efforts have been focused on the mid-surface abstraction, including the model decomposition based methods, Medial Axis Transform (MAT) based methods and Chordal Axis Transform (CAT) based methods.
Among the four methods, the two model decomposition-based methods were much more efficient than the other two methods.
I extend the model through decomposition methods to help clarify the association between earnings, job separations, and elasticity.
TDDM is another model decomposition-based method, which avoids costly ODE simulations by estimating the slopes of smooth piecewise polynomial functions that interpolate observed data.
Additive and multiplicative models are two common models in the decomposition method.
Here, we compare the ARIMA model with the decomposition method to observe the prediction results.
