Imagine you're trying to understand how things work together in a complex world, like how many friends you have influences your grades. This sounds like a puzzle best solved with statistics. Now, instead of just counting friends (that's like a two-dimensional problem), think of all the factors that might affect grades—friends, study hours, homework, parental support, and more. That's when you enter the realm of high-dimensional covariance matrices.

Surprisingly, most researchers focus on how accurate these matrices are. But what if big estimates mess up your statistical inferences? That's the big question this paper tackles. It dives into two key models: factor analysis and panel data models with interactive effects. Both need a clever tool called weighted principal components (WPC) to make estimations efficient.

Now, WPC isn't your average tool. It's based on a fancy covariance matrix estimator, thanks to Fan and their team (2013). The tricky part? Existing rules for large estimates don’t always work here. You need a special kind of consistency and a precise rate of convergence. That’s what this paper explores.

To see if this new WPC works, the researchers tried it out on U. S. divorce rates. Guess what? It showed that divorce-law changes have a big impact on divorce rates. Plus, it gave clearer estimates and more reliable confidence intervals than the usual methods.

Covariance Matters: A Closer Look at Its Impact

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