Hi Martin,
Yes, its Pischke.
In stata:
help moulton
rgds,
Gaby
--- On Wed, 6/24/09, Martin Weiss <[email protected]> wrote:
> From: Martin Weiss <[email protected]>
> Subject: st: AW: moulton factor correction
> To: [email protected]
> Date: Wednesday, June 24, 2009, 7:35 AM
>
> <>
>
>
>
> What is the precise reference for "Angrist and Piscke
> (2009)"? (Guess that
> should be Pischke in any event)
>
>
>
> HTH
> Martin
>
>
> -----Ursprüngliche Nachricht-----
> Von: [email protected]
> [mailto:[email protected]]
> Im Auftrag von Ana Gabriela
> Guerrero Serdan
> Gesendet: Mittwoch, 24. Juni 2009 14:15
> An: stata listserve
> Betreff: st: moulton factor correction
>
> Dear all,
>
> I am trying to understand why in this example when using
> the moulton
> correction developped by Angrist and Piscke (2009) I get
> some SE that are
> lower then when using the cluster option. I thought that
> the moulton
> correction will increase SE, but seems not to be the case
> for all
> coefficients. Can someone help me interpret what is going
> on?
>
>
> use http://www.ats.ucla.edu/stat/stata/seminars/svy_stata_intro/srs,
> clear
> regress api00 growth emer yr_rnd, robust
> regress api00 growth emer yr_rnd, cl(dnum)
> moulton api00 growth emer yr_rnd, cl(dnum)
> moulton api00 growth emer yr_rnd, cl(dnum) moulton
> loneway api00 dnum
>
>
> results below:
>
> thanks,
> Gaby
>
> use http://www.ats.ucla.edu/stat/stata/seminars/svy_stata_intro/srs,
> clear
>
>
> . regress api00 growth emer yr_rnd,
> robust
>
> Linear regression
>
> Number of obs =
> 309
>
>
>
> F(
> 3, 305) =
> 33.15
>
>
>
> Prob > F
> =
> 0.0000
>
>
>
> R-squared
> =
> 0.2770
>
>
>
> Root MSE
> =
> 111.54
>
> ----------------------------------------------------------------------------
> --
>
> |
> Robust
> api00 |
> Coef. Std. Err. t
> P>|t| [95% Conf.
> Interval]
> -------------+--------------------------------------------------------------
> --
> growth | -.1027121
> .195606 -0.53 0.600
> -.4876202
> .2821961
> emer |
> -5.444932 .5532104
> -9.84 0.000 -6.533524
> -4.35634
> yr_rnd |
> -51.07569 20.28729
> -2.52 0.012 -90.99645
> -11.15493
> _cons
> | 740.3981 12.13784
> 61.00 0.000
> 716.5136
> 764.2826
> ----------------------------------------------------------------------------
> --
>
> . regress api00 growth emer yr_rnd, cl(dnum)
>
> Linear regression
>
> Number of obs =
> 309
>
>
>
> F(
> 3, 185) =
> 19.72
>
>
>
> Prob > F
> =
> 0.0000
>
>
>
> R-squared
> =
> 0.2770
>
>
>
> Root MSE
> =
> 111.54
>
>
>
> (Std. Err. adjusted for 186 clusters in
> dnum)
> ----------------------------------------------------------------------------
> --
>
> |
> Robust
> api00 |
> Coef. Std. Err. t
> P>|t| [95% Conf.
> Interval]
> -------------+--------------------------------------------------------------
> --
> growth |
> -.1027121 .2291703
> -0.45 0.655 -.5548352
> .3494111
> emer |
> -5.444932 .7293969
> -7.46 0.000 -6.883938
> -4.005927
> yr_rnd |
> -51.07569 22.83615
> -2.24 0.027 -96.12844
> -6.022935
> _cons
> | 740.3981 13.46076
> 55.00 0.000
> 713.8418
> 766.9544
> ----------------------------------------------------------------------------
> --
>
> . moulton api00 growth emer yr_rnd,
> cl(dnum)
>
> OLS Regression: standard errors
>
> Number of obs =
> 309
> adjusted for cluster effects using Moulton
> R-squared =
> 0.2770
>
>
>
> Adj R-squared =
> 0.2698
> Number of clusters (dnum) = 186
>
> Root MSE =
> 111.541
>
> ----------------------------------------------------------------------------
> --
> api00 |
> Coef. Std. Err. t
> P>|t| [95% Conf.
> Interval]
> -------------+--------------------------------------------------------------
> --
> growth |
> -.1027121 .2455165
> -0.42 0.676 -.5858326
> .3804084
> emer |
> -5.444932 .7128514
> -7.64 0.000 -6.847662
> -4.042203
> yr_rnd |
> -51.07569 20.57651
> -2.48 0.014 -91.56558
> -10.58579
> _cons
> | 740.3981 17.78252
> 41.64 0.000
> 705.4061
> 775.39
> ----------------------------------------------------------------------------
> --
> Intraclass correlation in growth =
> 0.2567
> Intraclass correlation in emer
> = 0.5444
> Intraclass correlation in yr_rnd =
> 0.0494
> Intraclass correlation in residual = 0.3775
>
> . moulton api00 growth emer yr_rnd,
> cl(dnum) moulton
>
> OLS Regression: standard errors
>
> Number of obs =
> 309
> adjusted for cluster effects using Moulton
> R-squared =
> 0.2770
>
>
>
> Adj R-squared =
> 0.2698
> Number of clusters (dnum) = 186
>
> Root MSE =
> 111.541
>
> ----------------------------------------------------------------------------
> --
> api00 |
> Coef. Std. Err. t
> P>|t| [95% Conf.
> Interval]
> -------------+--------------------------------------------------------------
> --
> growth |
> -.1027121 .2203333
> -0.47 0.641 -.5362779
> .3308538
> emer |
> -5.444932 .6285286
> -8.66 0.000 -6.681734
> -4.208131
> yr_rnd |
> -51.07569 20.53625
> -2.49 0.013 -91.48636
> -10.66502
> _cons
> | 740.3981 15.49653
> 47.78 0.000
> 709.9044
> 770.8917
> ----------------------------------------------------------------------------
> --
> Intraclass correlation in growth =
> 0.1107
> Intraclass correlation in emer
> = 0.4466
> Intraclass correlation in yr_rnd =
> 0.0794
> Intraclass correlation in residual = 0.2204
>
>
>
> . loneway api00 dnum
>
>
> One-way Analysis of Variance for api00:
>
>
>
> Number of
> obs = 310
>
>
>
> R-squared = 0.7392
>
> Source
> SS
> df MS
> F Prob
> > F
> -------------------------------------------------------------------------
> Between dnum
> 3879736.2 186
> 20858.797 1.87
> 0.0001
> Within dnum
> 1369122.1 123 11131.074
> -------------------------------------------------------------------------
> Total
> 5248858.3 309
> 16986.596
>
> Intraclass
> Asy.
> correlation
> S.E. [95% Conf.
> Interval]
>
> ------------------------------------------------
> 0.34737
> 0.08516
> 0.18045 0.51428
>
> Estimated SD of dnum
> effect
> 76.97107
> Estimated SD within
> dnum
> 105.5039
> Est. reliability of
> a dnum mean 0.46636
>
>
>
>
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