Kit Baum replied:
> But in including robust to get H-AC std errors you took out the -small-
> option, so you're comparing t-stats with z-stats.
Quite right. First problem solved. To use my own data:
. newey2 conch el1983-el2001 if !inlist(_n, 379) [aw=weight], lag(1) force
Regression with Newey-West standard errors Number of obs = 3451
maximum lag : 1 F( 5, 3445) = 1747.84
Prob > F = 0.0000
----------------------------------------------------------------------------
| Newey-West
conch | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-----------+----------------------------------------------------------------
el1983 | -9.470202 .2210995 -42.83 0.000 -9.903702 -9.036703
el1987 | -8.534744 .2023425 -42.18 0.000 -8.931467 -8.13802
el1992 | -8.850865 .171069 -51.74 0.000 -9.186272 -8.515458
el1997 | -19.38584 .2086105 -92.93 0.000 -19.79485 -18.97682
el2001 | -7.284097 .1915389 -38.03 0.000 -7.659638 -6.908555
_cons | 7.956494 .1334872 59.60 0.000 7.694772 8.218216
----------------------------------------------------------------------------
. ivreg2 conch el1983-el2001 if !inlist(_n, 379) [aw=weight], bw(2) robust
small
(sum of wgt is 3.4499e+03)
OLS regression with robust standard errors
------------------------------------------
Heteroskedasticity and autocorrelation-consistent statistics
kernel=Bartlett; bandwidth=2
time variable (t): election
group variable (i): pano
Number of obs = 3451
F( 5, 3445) = 1747.84
Prob > F = 0.0000
Total (centered) SS = 152718.5772 Centered R2 = 0.7237
Total (uncentered) SS = 155933.7759 Uncentered R2 = 0.7294
Residual SS = 42188.62656 Root MSE = 3.5
----------------------------------------------------------------------------
| Robust
conch | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-----------+----------------------------------------------------------------
el1983 | -9.470202 .2210995 -42.83 0.000 -9.903702 -9.036703
el1987 | -8.534744 .2023425 -42.18 0.000 -8.931467 -8.13802
el1992 | -8.850865 .171069 -51.74 0.000 -9.186272 -8.515458
el1997 | -19.38584 .2086105 -92.93 0.000 -19.79485 -18.97682
el2001 | -7.284097 .1915389 -38.03 0.000 -7.659638 -6.908555
_cons | 7.956494 .1334872 59.60 0.000 7.694772 8.218216
----------------------------------------------------------------------------
Thanks very much for the solution. This is excellent (especially as
-ivreg2- accepts -pweight-s), and I can work out most goodness-of-fit
diagnostics from there.
But as my second query notes, it's often a good idea to control for
'higher-level' clustering effects. In my data, constituencies can be
grouped into regions. As it says in -whelp ivreg2-:
"...-cluster- standard errors are robust to both arbitrary
heteroskedasticity and arbitrary intra-group correlation..."
I think I have it right that the first of these (on its own) is dealt with
by using the -robust- option; the second refers to what I'm now looking to
control. As I said a few days ago, this is easily done with -areg-, but
when I try to do this using -ivreg2-, I get:
. ivreg2 conch el1983-el2001 if !inlist(_n, 379) [aw=weight], bw(2) robust
cluster(region) small
cannot use HAC kernel estimator with -cluster- option
r(198);
This is odd, since later on in -whelp ivreg2-, it explicitly states that
the -robust- and -cluster()- options can be used together:
"...-robust- combined with -cluster()- further allows residuals which are
not independent within cluster (although they must be independent between
clusters)."
This is _precisely_ what I'm looking for, since including these
higher-level fixed effects as variables is not the proper way to solve
this issue. It also says that specifying -cluster()- implies -robust- (as
well as saying that -pweight-s can also be used), but unfortunately, I
get:
. ivreg2 conch el1983-el2001 if !inlist(_n, 379) [pw=weight], bw(2)
cluster(region) small
cannot use HAC kernel estimator with -cluster- option
r(198);
I get exactly the same errors if I use the -garmit- dataset. I really hope
there is a workaround to this, as I don't think I'm doing a lot wrong
here.
CLIVE NICHOLAS |t: 0(044)7903 397793
Politics |e: [email protected]
Newcastle University |http://www.ncl.ac.uk/geps
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