The article discusses how to estimate large-scale models with mixed-frequency data. By using a factor stochastic volatility model, the researchers were able to make the computations more efficient. This model helped improve the simulation process and allowed for parallel sampling of regression parameters. The researchers applied this model to US data with 20, 34, and 119 variables, showing its effectiveness in handling complex data sets.