The article discusses how to estimate the trend and cycle of economic data like GDP using a large set of information. The researchers use a method called the Beveridge-Nelson decomposition with a vector autoregression. They figure out which variables are important by looking at how different errors contribute to the trend and cycle. They also use Bayesian shrinkage to prevent overfitting when dealing with lots of data. Their study on the US economy shows that factors like unemployment rate, inflation, and housing starts, among others, provide useful information for estimating the output gap.