Comments (5)
Block size has to be at least 1. int(max(size, 1))
would work.
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45892.0
is not an integer. Make sure size is an integer, e.g., size=int(size)
.
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As you can see the bs_opt.bootstrap(n_samples) seems to be calling an IID boostrapping. is this supposed to be correct?
Yes. The bootstrap
method calls private internal methods that differ across classes, so that the bootstrap
never needs to be overwritten.
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Going to close as answered, but please fell free to continue to post if you this isn't clean.
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Thanks. There is an issue with this solution (i.e. size=int(size)
). Very often I use the optimal block size provided by the library and the result is a float, like 0.40
. This solution will make the optimal size block zero and the bootstrap will fail again.
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Related Issues (20)
- Exogenous variable in the Volatility Equation HOT 1
- ENH: Add NGARCH specification to set of volatility processes
- Issue when using arch.bootstrap.MCS HOT 9
- My scratch implementation does not match the result for EGARCH HOT 1
- Tests fail: ImportError while loading conftest '/usr/ports/science/py-arch/work-py39/arch-6.1.0/arch/conftest.py'. HOT 3
- Probably bug in FIGARCH implementation with horizon > 1 HOT 1
- ModuleNotFoundError: No module named 'arch' when importing arch_model HOT 7
- add topic garch
- Clarification on `.simulated_variances` attribute of `ARCHModelForecast` object. HOT 3
- `arch_model` estimator is forecasting same value irrespective of the `horizon` parameter. HOT 2
- [DOC] Links to example notebooks for unit root tests and cointegration testing analysis HOT 1
- Are we using two-step approach for estimation? HOT 2
- volatility forecast in comparison with realized volatility HOT 3
- How to modify GARCH model to incorporate new terms ? HOT 1
- Source for Long Run Covariance Estimator
- There is no _version.py in arch.
- Details on Hansen's Skewed T
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