A new type of statistical model called Generalized Autoregressive Conditional Heteroskedasticity (GARCH) has been developed to better predict changes in variance over time. This model builds on the Autoregressive Conditional Heteroskedasticity (ARCH) process by incorporating past conditional variances into the current variance equation. By allowing for changing variance based on past errors, GARCH models can more accurately capture uncertainty in data, such as inflation rates. The researchers have outlined the conditions for this model to work and have shown how it can be estimated and tested using maximum likelihood methods. An empirical example related to inflation uncertainty demonstrates the effectiveness of GARCH models in capturing changing variance over time.