If you’re building a time series model and you want any hope of statistical significance, do your analysis on non-overlapping periods.https://twitter.com/macrocephalopod/status/1460302495507525635 …
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macrocephalopod Retweeted macrocephalopod
If you’re building a time series model and you want any hope of statistical significance, do your analysis on non-overlapping periods.https://twitter.com/macrocephalopod/status/1460302495507525635 …
macrocephalopod added,
1/2 Hmm I get that intuitively the overlap means that not all samples are independent so true variance should be higher than sample variance (and thus t-stat lower), but it also intuitively feels like completely disjoint subperiods throws away info.
2/2 For vol estimation, the Hodges and Tompkins correction can be used to correct this bias (the bias in the variance of vol). Do you know if there's a similar method for general regressions?
in a regression the traditional fix for this would be something like newey west or hansen-hodrick standard errors. instead of assuming the residuals to be independent they also include the autocovariance terms in the estimate. you may find this useful: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3142575 …
That's the paper I was trying and failing to find earlier, thank you!
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