st: RE: SE/CI for difference in transition matrix row proportions after -svy tabulate- twoway

["Scholes, Shaun" <s.scholes@ucl.ac.uk>]

I'm having an early evening junior moment (and about to log off) but could you not:

svy: mean bu_SA, over(Lbu_SA)

and use lincom to estimate the difference, and obtain SE/CIs for the difference using that? 

Best wishes
Shaun




-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of S.Jenkins@lse.ac.uk
Sent: 03 April 2012 18:13
To: statalist@hsphsun2.harvard.edu
Subject: st: SE/CI for difference in transition matrix row proportions after -svy tabulate- twoway

I'm having an early evening senior moment, and can't figure out how to calculate, from saved results after -svy tabulate-, the difference between two elements of a 2x2 transition matrix and the associated SE/CI. I've been browsing svy help and documentation, and can't find the answer directly. Part of my problem is not understanding precisely what is stored in the saved variance-covariance matrix.

Below is example output from a simplified example. I have a binary measure of receipt at t-1 and at t, and cross-tabulate them. The two transition proportions of interest are the entry rate, P(0,1), which is estimated to be 0.0335 in the example, and the stayer rate, P(1,1), which is estimated to be 0.6553 in the example. I want not only the the difference P(1,1) - P(0,1), but also a SE/CI for the difference, which I was assuming I could calculate from the saved results. Suggestions please.


. svy: tabulate Lbu_SA bu_SA, row se
(running tabulate on estimation sample)

Number of strata   =         1                  Number of obs      =
75988
Number of PSUs     =      9036                  Population size    =
75988
                                                Design df          =
9035

-------------------------------------------
1:R's BU  |
receives  |
IS|UBIS|U |
B|JSA,    |1:R's BU receives IS|UBIS|UB|JSA
t-1       |         0          1      Total
----------+--------------------------------
        0 |     .9665      .0335          1
          | (8.2e-04)  (8.2e-04)           
          | 
        1 |     .3447      .6553          1
          |   (.0095)    (.0095)           
          | 
    Total |     .9255      .0745          1
          |   (.0023)    (.0023)           
-------------------------------------------
  Key:  row proportions
        (linearized standard errors of row proportions)

  Pearson:
    Uncorrected   chi2(1)         =  2.62e+04
    Design-based  F(1, 9035)      =  1.47e+04     P = 0.0000


. mat list e(b)

e(b)[1,4]
          p11        p12        p21        p22
y1  .96649524  .03350476  .34470377  .65529623

. mat list e(V_row)

symmetric e(V_row)[2,2]
               r1:         r2:
               r1          r1
r1:r1   4.394e-06
r2:r1  -4.394e-06   4.394e-06


. mat list e(V)

symmetric e(V)[4,4]
            p11         p12         p21         p22
p11   6.751e-07
p12  -6.751e-07   6.751e-07
p21   1.477e-07  -1.477e-07   .00008959
p22  -1.477e-07   1.477e-07  -.00008959   .00008959




Stephen
------------------
Professor Stephen P. Jenkins <s.jenkins@lse.ac.uk> Department of Social Policy and STICERD London School of Economics and Political Science Houghton Street, London WC2A 2AE, UK
Tel: +44(0)20 7955 6527
Changing Fortunes: Income Mobility and Poverty Dynamics in Britain, OUP 2011, http://ukcatalogue.oup.com/product/9780199226436.do
Survival Analysis Using Stata:
http://www.iser.essex.ac.uk/survival-analysis
Downloadable papers and software: http://ideas.repec.org/e/pje7.html


Please access the attached hyperlink for an important electronic communications disclaimer: http://lse.ac.uk/emailDisclaimer

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