# Moment condition heuristics

A theorem in statistics is a theorem in mathematics, and of course the conclusion only really holds if the requirements hold. I can’t say much about Bayesian literaure, but the papers I have been reading recently all have moment conditions. A classic exmaple is of course the (Lindeberg-Lévy) Central Limit Theorem, where

\[\text{var}(X_i) = \sigma^2 < \infty.\]This classic formulation of Central Limit Theorem is often used in estimating the population mean when we only have a small sample, where we get a point estimate and an estimated standard error, then apply the normal approximation. But how do we know if the theorem “applies”? I’ve usually gotten two answers, or a combination of both:

- If you are so worried about the normal approximation, use bootstrap.
- The theorem always apply, since you technically have a finite population, so you technically have a finite variance regardless.
- (Occasionally) The theorem always apply, and how well the approximation is governed by Berry–Esseen theorem. (I guess this is somewhat helpful, but then I’m stuck figuring out what the third moment is.)

For the first answer, my intuition is that when normal approximation is poor, bootstrap (at least standard percentile bootstrap) will be poor too, since the proofs of bootstrap I am familiar with goes through normal distributions too (e.g. van der Vaart, Chapter 23.2.1).

For the second answer, I still want to know if the approximation is good. Philosophically, this is similar to doing high-dimensional linear regression — when we have \(n = 1000\) and \(p = 100\), should we be looking at results on \(p = O(n)\) or \(p = O(1)\)? The idea here is the same: when we look at our data, we can sometimes tell that the normal approximation is going to fail if the top 10% of your sample accounts adds up to 90% of the sum. Can we have some heuristic that these moment conditions are going to fail?

More generally, I want to be able to look “empirically check” for moment conditions that look like

\[\frac{1}{n} \sum_{i=1}^n |X_i|^\beta = o(n^\gamma).\]In most cases I’ve encountered, \(\beta\) would be an integer, and I am including a \(\gamma\) there as well to include cases like Guo and Basse (2021), Example 1:

\[\frac{1}{n} \sum_{i=1}^n X_i^4 = o(n).\]Below are back-of-the-envelope calculations that I’ve been using recently to reason around these conditions. We are going to start with the stable distribution, with tail index \(\alpha\). Recall that if \(X, Y \sim \text{Stable}(\alpha)\) independently, then \(X + Y \sim 2^{1/\alpha} \cdot \text{Stable}(\alpha)\). Now in cases where the generalized Central Limit Theorem works, we will be able to get, with some scaling and some choice of \(\alpha\),

\[\frac{1}{n/2} \sum_{i=1}^{n/2} |X_i|^\beta, \frac{1}{n/2} \sum_{i=n/2 + 1}^{n} |X_i|^\beta \sim \text{Stable}(\alpha),\]and combining them will get

\[\frac{1}{n} \sum_{i=1}^n |X_i|^\beta \sim 2^{1/\alpha - 1} \cdot \text{Stable}(\alpha).\]Repeating this ad infinitum, we have

\[\frac{1}{n} \sum_{i=1}^n |X_i|^\beta = O_p((2^{1/\alpha - 1})^{\log_2 n}) = O_p(n^{1/\alpha - 1}).\]So for example, in the case of Guo and Basse (2021), Example 1, we will need \(X_i^\beta\) to be no more heavy-tailed than \(\alpha = 1/2\). But now, since \(X \sim \text{Stable}(\alpha)\) is roughly the boundary where \(\mathbb{E}[X^\alpha]\) exists, Guo and Basse (2021) should be roughly the same as asking the tail index to be at least 2, pretty much the same as asking that Central Limit Theorem applies.

I think the heuristics above can be made a bit more rigorous too, probably through \(\mathbb{P}[X > n^a] > n^{-b}\) for some choice of \(a\) and \(b\), these choices of \(a\) and \(b\) can perhaps be compared to a generalized Pareto distribution.

Finally to check this, without getting into the subtleties of Hill estimator or Clauset et al. (2009), we can look at some high quantiles and take the ratio. For example, a rough estimate of the tail index would be \(1 / \log_{10}(q_{0.999} / q_{0.99})\).