Ok, thanks for the suggestions. I will look into that. And you are absolutely
right that I should have been more clear about what type of weighting I want.
So to clarify: I run time series regressions of returns of company i on two
different sets of explanatory variables. Then I extract the respective
intercepts of the two regressions and take the difference between both. I
repeat this for the whole sample of companies and then compute the market value
weighted average of those differences.
On 21 Nov 2014, at 19:18, David Winsemius <dwinsem...@comcast.net> wrote:
>
> On Nov 21, 2014, at 6:52 AM, ivan wrote:
>
>> I am aware of the fact that bootstrapping produces different CIs with every
>> run. I still believe that there is a difference between both types of
>> procedures. My understanding is that setting "w" in the boot() function
>> influences the "importance" of observations or how the bootstrap selects the
>> observations. I.e, observation i does not have the same probability of being
>> chosen as observation j when "w" is defined in the boot() function. If you
>> return res_boot you will notice that with "w" being set in the boot()
>> function, the function call states "weighted bootstrap". If not, it states
>> "ordinary nonparametric bootstrap". But maybe I am wrong.
>
> OK. So in the the second call w affects the probability of a case being sent
> to the boot-function as well as being used in the boot-function; while with
> the "non-weighted call" the w's are only affecting the individual mean
> estimates. So the second one is different. And as I suggested earlier you
> never described the goals of the investigation or the meaning of the
> variables.
>
> I can tell you that when Davison and Hinkley offered examples of using a
> bootstrap for a weighted bootstrap mean, they compared a stratified analysis
> with an example where the weighting was only used on the inner function
> (example 3.2, practical 3.14 pp 72, 131 of their book) with one where the
> strata parameter was used. But so far I don't think you have ever described
> what sort of weights these actually are. In that example the weights were the
> inverse variances of the sample groups. They didn't use a 'weights' parameter
> in the boot call. I'm do not know if it was part of the S package that was
> being used at the time.
>
> I tried to find an example of a weighted bootstrap in V&R 4e but did not see
> one. Prof Ripley is the maintainer of the boot package. In the V&R book,
> Angelo Canty is given the credit for writing the boot package for S. I think
> you should consult the code, first. And you should also look at the `stype`
> parameter where "w" is one option.
>
> --
> David.
>
>>
>> On Thu, Nov 20, 2014 at 8:19 PM, David Winsemius <dwinsem...@comcast.net>
>> wrote:
>>
>> On Nov 20, 2014, at 2:23 AM, i.petzev wrote:
>>
>>> Hi David,
>>>
>>> sorry, I was not clear.
>>
>> Right. You never were clear about what you wanted and your examples was so
>> statistically symmetric that it is still hard to see what is needed. The
>> examples below show CI's that are arguably equivalent. I can be faulted for
>> attempting to provide code that produced a sensible answer to a vague
>> question to which I was only guessing at the intent.
>>
>>
>>> The difference comes from defining or not defining �w� in the boot()
>>> function. The results with your function and your approach are thus:
>>>
>>> set.seed(1111)
>>> x <- rnorm(50)
>>> y <- rnorm(50)
>>> weights <- runif(50)
>>> weights <- weights / sum(weights)
>>> dataset <- cbind(x,y,weights)
>>>
>>> vw_m_diff <- function(dataset,w) {
>>> differences <- dataset[w,1]-dataset[w,2]
>>> weights <- dataset[w, "weights"]
>>> return(weighted.mean(x=differences, w=weights))
>>> }
>>> res_boot <- boot(dataset, statistic=vw_m_diff, R = 1000, w=dataset[,3])
>>> boot.ci(res_boot)
>>>
>>> BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
>>> Based on 1000 bootstrap replicates
>>>
>>> CALL :
>>> boot.ci(boot.out = res_boot)
>>>
>>> Intervals :
>>> Level Normal Basic
>>> 95% (-0.5657, 0.4962 ) (-0.5713, 0.5062 )
>>>
>>> Level Percentile BCa
>>> 95% (-0.6527, 0.4249 ) (-0.5579, 0.5023 )
>>> Calculations and Intervals on Original Scale
>>>
>>> ********************************************************************************************************************
>>>
>>> However, without defining �w� in the bootstrap function, i.e., running an
>>> ordinary and not a weighted bootstrap, the results are:
>>>
>>> res_boot <- boot(dataset, statistic=vw_m_diff, R = 1000)
>>> boot.ci(res_boot)
>>>
>>> BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
>>> Based on 1000 bootstrap replicates
>>>
>>> CALL :
>>> boot.ci(boot.out = res_boot)
>>>
>>> Intervals :
>>> Level Normal Basic
>>> 95% (-0.6265, 0.4966 ) (-0.6125, 0.5249 )
>>
>> I hope you are not saying that because those CI's are different that there
>> is some meaning in that difference. Bootstrap runs will always be
>> "different" than each other unless you use set.seed(.) before the runs.
>>
>>>
>>> Level Percentile BCa
>>> 95% (-0.6714, 0.4661 ) (-0.6747, 0.4559 )
>>> Calculations and Intervals on Original Scale
>>>
>>> On 19 Nov 2014, at 17:49, David Winsemius <dwinsem...@comcast.net> wrote:
>>>
>>>>>> vw_m_diff <- function(dataset,w) {
>>>>>> differences <- dataset[w,1]-dataset[w,2]
>>>>>> weights <- dataset[w, "weights"]
>>>>>> return(weighted.mean(x=differences, w=weights))
>>>>>> }
>>>
>>
>> David Winsemius
>> Alameda, CA, USA
>>
>>
>
> David Winsemius
> Alameda, CA, USA
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