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st: RE: IVREG2 vs. REG/CLUSTER2 - Difference in number of observations reported in regressions
From
"Schaffer, Mark E" <[email protected]>
To
<[email protected]>
Subject
st: RE: IVREG2 vs. REG/CLUSTER2 - Difference in number of observations reported in regressions
Date
Mon, 10 Sep 2012 14:13:55 +0100
Ulrich,
This is odd ... can you first check that you have the latest version of
ivreg2 installed? From within Stata, type
which ivreg2, all
You should have
*! ivreg2 3.1.04 19mar2012
*! authors cfb & mes
*! see end of file for version comments
If you have the latest version, then please contact me offline and we
will try to work out what is going on.
Yours,
Mark (ivreg2 coauthor)
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of
> Hofbaur, Ulrich
> Sent: Monday, September 10, 2012 1:51 PM
> To: [email protected]
> Subject: st: IVREG2 vs. REG/CLUSTER2 - Difference in number
> of observations reported in regressions
>
> Hi everybody,
>
> I am using the "ivreg2"-command to run an OLS-regression
> model and simultaneously allow for 2-way clustering in the
> SE-terms. So, the command is simply "ivreg2 y x,
> cluster(cs_id ts_id)". As it turns out STATA drops some
> observations (about 30 percent of the total sample; it keeps
> 1806 instead of 2618 obs.) when conducting the regression
> although the information is available. I have also tried the
> related "cluster2"-command and the ordinary "reg"-command.
> However, these commands are using the full set of
> observations. Does anyone know why this difference in number
> of observations reported in the regressions shows up?
>
> Help highly appreciated!
>
> Best,
> Ulrich
>
> The code is given below.
>
> ---------------------------------------------
> . reg car_m1_1 dv_chng
>
> Source | SS df MS Number
> of obs = 2618
> -------------+------------------------------ F( 1,
> 2616) = 69.36
> Model | .359573378 1 .359573378 Prob >
> F = 0.0000
> Residual | 13.5615124 2616 .005184064
> R-squared = 0.0258
> -------------+------------------------------ Adj
> R-squared = 0.0255
> Total | 13.9210858 2617 .005319483 Root
> MSE = .072
>
> --------------------------------------------------------------
> ----------------
> car_m1_1 | Coef. Std. Err. t P>|t|
> [95% Conf. Interval]
> -------------+------------------------------------------------
> ----------------
> dv_chng | .0602397 .0072331 8.33 0.000
> .0460565 .0744228
> _cons | -.0057141 .003311 -1.73 0.084
> -.0122064 .0007783
> --------------------------------------------------------------
> ----------------
>
> . ivreg2 car_m1_1 dv_chng
>
> OLS estimation
> --------------
>
> Estimates efficient for homoskedasticity only
> Statistics consistent for homoskedasticity only
>
> Number
> of obs = 1806
> F( 1,
> 1804) = 35.82
> Prob >
> F = 0.0000
> Total (centered) SS = 10.15831896
> Centered R2 = 0.0195
> Total (uncentered) SS = 12.13900806
> Uncentered R2 = 0.1795
> Residual SS = 9.960554106 Root
> MSE = .07426
>
> --------------------------------------------------------------
> ----------------
> car_m1_1 | Coef. Std. Err. z P>|z|
> [95% Conf. Interval]
> -------------+------------------------------------------------
> ----------------
> dv_chng | .0536313 .0089563 5.99 0.000
> .0360774 .0711853
> _cons | -.0098285 .0042637 -2.31 0.021
> -.0181852 -.0014719
> --------------------------------------------------------------
> ----------------
> Included instruments: dv_chng
> --------------------------------------------------------------
> ----------------
>
> . ivreg2 car_m1_1 dv_chng, cluster(cs_id ts_id)
>
> OLS estimation
> --------------
>
> Estimates efficient for homoskedasticity only
> Statistics robust to heteroskedasticity and clustering on
> cs_id and ts_id
>
> Number of clusters (cs_id) = 1149 Number
> of obs = 1806
> Number of clusters (ts_id) = 44 F( 1,
> 43) = 24.80
> Prob >
> F = 0.0000
> Total (centered) SS = 10.15831896
> Centered R2 = 0.0195
> Total (uncentered) SS = 12.13900806
> Uncentered R2 = 0.1795
> Residual SS = 9.960554106 Root
> MSE = .07426
>
> --------------------------------------------------------------
> ----------------
> | Robust
> car_m1_1 | Coef. Std. Err. z P>|z|
> [95% Conf. Interval]
> -------------+------------------------------------------------
> ----------------
> dv_chng | .0536313 .0106425 5.04 0.000
> .0327725 .0744902
> _cons | -.0098285 .0052442 -1.87 0.061
> -.020107 .0004499
> --------------------------------------------------------------
> ----------------
> Included instruments: dv_chng
> --------------------------------------------------------------
> ----------------
>
> . cluster2 car_m1_1 dv_chng, fcluster(cs_id) tcluster(ts_id)
>
> Linear regression with 2D clustered SEs Number
> of obs = 2618
> F( 1,
> 2499) = 64.84
> Prob >
> F = 0.0000
> Number of clusters (cs_id) = 1568
> R-squared = 0.0258
> Number of clusters (ts_id) = 51 Root
> MSE = 0.0720
> --------------------------------------------------------------
> ----------------
> car_m1_1 | Coef. Std. Err. t P>|t|
> [95% Conf. Interval]
> -------------+------------------------------------------------
> ----------------
> dv_chng | .0602397 .0091375 6.59 0.000
> .0423219 .0781575
> _cons | -.0057141 .0040381 -1.42 0.157
> -.0136324 .0022042
> --------------------------------------------------------------
> ----------------
>
> SE clustered by cs_id and ts_id (multiple obs per cs_id-ts_id)
>
>
> . count if car_m1_1!=. & dv_chng!=. &cs_id!=. & ts_id!=.
> 2618
>
> . distinct cs_id
>
> | Observations
> Variable | total distinct
> --------------+----------------------
> cs_id | 2618 1568
>
> . distinct ts_id
>
> | Observations
> Variable | total distinct
> --------------+----------------------
> ts_id | 2618 51
>
> *
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>
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