Given a multivariate regression, in a form bellow, what would be the minimum number of observations ($n$)? $$\mathbf{Y}=\mathbf{X}\mathbf{B}+\mathbf{E},$$ where $\mathbf{Y}, \mathbf{X}$ and $\mathbf{E} \in R^{n\times m}$ $(n>m)$ and $\mathbf{B}\in R^{m \times m}$. You can treat each column of $\mathbf{Y}$ and $\mathbf{X}$ as a time series.

Based on Frank Harrell's book, Regression Modeling Strategies, if you expect to be able to detect reasonable-size effects with reasonable power, you need 10-20 observations per parameter (covariate) estimated. I wonder if this would still hold in multivariate regression rather univariate? In the univariate the dependent variable $Y$ is one time series (vector format) and in multivariate one, it consists of more than one time series (matrix format).

For more info on multivariate reg: This starting from p.43.


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I believe that it is better to think in terms of power to detect effects of interest, rather than rules of thumb. That said, if you want a quick-and-dirty baseline value, the standard rule of thumb is $N=10$ per parameter. Note that in a multivariate context, the number of parameters is greater than the number of covariates. Consider a multiple regression with one $Y$ and three $X$'s: that would be a minimum of $N=30$. However, if there were three $Y$'s, that would be $N=90$.

Note further that this ignores covariance parameters (people often care primarily about the means). I don't know of an analogous rule of thumb for covariances, but if you wanted reasonable estimates of the covariances, they will grow much more rapidly as the number of $Y$'s increases: with $p$ $Y$'s, the variance-covariance matrix will have $^{p(p+1)}/_2$ parameters to estimate for each $X$ variable. Moreover, since these depend on the means already being stable, you should think of those extra tens being added on top of the original requirement. That is, in our three $Y$, three $X$ example, you would need $N = 60$ (per $X$), times three $X$'s, is $N = 180$, plus the original ninety, is $N=270$. That certainly seems like a lot, but if you care about those parameters, there's a lot of them.


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