[R-sig-Geo] Maximum sparsity for spatial regression

[Josiah Parry]

Yes! This is perfect. Thank you so much.

On Mon, May 13, 2024 at 12:11 Roger Bivand <Roger.Bivand using nhh.no> wrote:

> Is Tony Smith's "Estimation Bias in Spatial Models with Strongly Connected
> Weight Matrices" at https://doi.org/10.1111/j.1538-4632.2009.00758.x
> helpful?
>
> Roger
>
> --
> Roger Bivand
> Emeritus Professor
> Norwegian School of Economics
> Postboks 3490 Ytre Sandviken, 5045 Bergen, Norway
> Roger.Bivand using nhh.no
>
> ________________________________________
> From: R-sig-Geo <r-sig-geo-bounces using r-project.org> on behalf of Josiah
> Parry <josiah.parry using gmail.com>
> Sent: 13 May 2024 17:12
> To: r-sig-Geo using r-project.org
> Subject: [R-sig-Geo] Maximum sparsity for spatial regression
>
> As I'm reading through Modern Spatial Econometrics in Practice, we assume
> the spatial weights matrix to be sparse. At one point they note that the
> contiguity matrix  for the US counties is 0.18% non-zero. But what %
> non-zero is too dense?
>
> I am wondering if there is any research or papers that document what a
> recommended upper bound of sparsity should be for one of these models? Is
> 10% non-zero too much or sufficient? I suspect the answer is, like most
> things, "it depends."
>
> But, thinking of a situation where someone might use a distance band to
> specify neighbors they might create a bandwidth that can encompass 50% or
> more of points if using max(knn=1) to specify the distance. I suspect using
> a kernel or IDW could reduce the weights close to zero making the impact
> minimal.
>
> Nonetheless, I'm curious if others have thought about this or written about
> it!
>
> Thanks,
>
> Josiah
>
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