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Re: st: Bootstrapping Malmquist Poductivity Index


From   Nick Cox <njcoxstata@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Bootstrapping Malmquist Poductivity Index
Date   Fri, 30 Nov 2012 15:46:16 +0000

It is unclear quite what you are doing. The -summarize- example does
not seem closely related to what you describe (although note that it
clearly refers to the median, and has nothing to do with the mean).
The allusion to DEA may or may not be an allusion to

st0193 from http://www.stata-journal.com/software/sj10-2
    SJ10-2 st0193.  Data Envelopment Analysis / Data Envelopment Analysis / by
    Ji, Yong-Bae, Korea National Defense University, / Republic of Korea /
    Lee, Choonjoo, Korea National Defense University, / Republic of Korea /
    Support:  sarang64@snu.ac.kr, sarang90@kndu.ac.kr, /

Either way, you are asked to be precise about what commands you are
using and (if they are user-written) to explain where they come from.

What you need is a program that emits the geometric mean as an e-class
result or an r-class result. If you are producing one geometric mean
for all panels that is one parameter; if there is a separate geometric
mean for each panel that is several parameters.

However, there will be some dependence structure either way that may
be problematic for -bootstrap-, but as everything depends on the
details of what you are doing it is difficult to give further precise
advice.

Nick

On Fri, Nov 30, 2012 at 1:03 PM, Yaseen Ghulam <Yaseen.Ghulam@port.ac.uk> wrote:

>  I have calculated malmquist productivity index using DEA. I want to bootstrap the index in Stata. I have unbalanced panel (14 firms and 26 years). I have seen a general command like
>
> webuse auto
> bootstrap r(p50), reps(1000) seed(1234): summarize mpg, detail
>
> I believe above command calculates standard error for median or mean by using 1000 replications. I am interested in calculating standard error of geometric mean by taking into account panel structure of the data (which above command probably does not) and then test whether geometric mean is significantly different than unity or not.

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