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People are always writing something ))) But it's not always clear why.... apparently they just know how to write....))))
Published articles are a prerequisite for PhDs and the like. So they write for a tick))
Suspicions of ostentation aside.
This is the first time I've seen an article that offers concrete use of multiple TFs instead of blah blahs from AA.
I read it)
Excuse me, but the article does not stand up to criticism. Firstly, I did not see a word about checking the statistical significance of the obtained deviations from the null-hypothesis. Secondly, the deviations themselves are so negligible that I would be afraid to stutter about any positive result of the "study"... In short, it seems to me that the fellow from the Bundesbank was just about to defend himself, so he wrote an article with the help of 2 of his cronies))))
I read it)
Excuse me, but the article does not stand up to criticism. Firstly, I did not see a word about checking the statistical significance of the obtained deviations from the null-hypothesis. Secondly, the deviations themselves are so negligible that I would hesitate to speak about any positive result of the "study"... In short, it seems to me that the fellow from the Bundesbank was just about to defend himself, so he wrote an article with the help of 2 of his cronies))))
Depends on where the stovepipe is. If Elder, then progress. On this forum the furnace is this one
If anything, I would rather look in this direction to see how high-frequency last period data can affect the accuracy of a regression model built on low-frequency data. Another variant - to try to use an irregular timeframe for regression: in application to Elder and in the presence of low-frequency data it makes sense, and there are suspicions that such a model will be at least an order of magnitude more accurate. And may be even more profitable)))
(About non-uniform meshes - you can draw a distant analogy to the methods of numerical integration; those who know, know that choice of Gaussian meshes allows to raise the approximation order from n to 2*n-1 incomparison with interpolation methods at the same number of knots).
If anything, I would rather look in this direction to see how high-frequency last period data can affect the accuracy of a regression model built on low-frequency data. Another variant - to try to use an irregular timeframe for regression: in application to Elder and in the presence of low-frequency data it makes sense, and there are suspicions that such a model will be at least an order of magnitude more accurate. And may be even more profitable))).
(About non-uniform meshes - you can draw a distant analogy to the methods of numerical integration; those who know, know that choice of Gaussian meshes allows to raise the approximation order from n to 2*n-1 incomparison with interpolation methods with the same number of nodes).
I was doing some exercises with noises from two different smoothing. Their difference (difference in the noise!) turned out to be extremely beautiful
And here's a test of the unit root.
How do you like it?
Don't you think the difference in noise from the two smoothings = the difference in the smoothed series themselves? )))
There's something there. But not the same thing. The difference of two smoothings is an upgraded MACD - in theory a differentiable function. But the difference of two noises is inherently noise, after all
There is something. But not the same thing. The difference of the two smoothings is an upgraded MACD - in theory a differentiable function. But the difference of two noises is, in theory, noise, after all