I am having trouble diagnosing a collinearity problem. Observations in my dataset are counties across years, ranging from 1996 to 2006. I am running a regression with county fixed effects. Intent, defier, m0, m1, m2 and m3 are binary variables and running is an integer that ranges from 0 to a 100.
-areg- drops some of the variables due to collinearity:
Code:
* areg drops variables
# delimit ;
areg ${outcome}
1.intent#0.defier#1.m0
l1.1.intent#l1.0.defier#1.m1
l2.1.intent#l2.0.defier#1.m2
l3.1.intent#l3.0.defier#1.m3
c.running#0.defier#1.m0
l1.c.running#l1.0.defier#1.m1
l2.c.running#l2.0.defier#1.m2
l3.c.running#l3.0.defier#1.m3
0.defier#m0
0.l1.defier#m1
0.l2.defier#m2
0.l3.defier#m3
if inrange(year,1996,2006)
& insample
, cluster(cty) absorb(cty);
# delimit cr
note: 0L2.defier#1.m2#cL2.running omitted because of collinearity
note: 0L3.defier#1.m3#cL3.running omitted because of collinearity
Linear regression, absorbing indicators Number of obs = 1,100
F( 10, 99) = 11.86
Prob > F = 0.0000
R-squared = 0.5210
Adj R-squared = 0.4683
Root MSE = 1.4812
(Std. Err. adjusted for 100 clusters in cty)
-------------------------------------------------------------------------------------
| Robust
unemp_rate | Coef. Std. Err. t P>|t| [95% Conf. Interval]
--------------------+----------------------------------------------------------------
intent#defier#m0 |
1 0 1 | -.9089798 .678034 -1.34 0.183 -2.254346 .4363869
|
L.intent#L.defier#|
m1 |
1 0 1 | -1.139256 .6345052 -1.80 0.076 -2.398252 .1197395
|
L2. |
intent#|
L2.defier#m2 |
1 0 1 | -1.0513 .492405 -2.14 0.035 -2.028339 -.0742618
|
L3. |
intent#|
L3.defier#m3 |
1 0 1 | -1.969312 1.007312 -1.96 0.053 -3.968037 .0294126
|
defier#m0#c.running |
0 1 | -.0625051 .0289846 -2.16 0.033 -.1200169 -.0049933
|
L.defier#m1#|
cL.running |
0 1 | -.0364417 .0252344 -1.44 0.152 -.0865122 .0136287
|
L2.defier#m2#|
cL2.running |
0 1 | 0 (omitted)
|
L3.defier#m3#|
cL3.running |
0 1 | 0 (omitted)
|
defier#m0 |
0 1 | -.0751216 .3238042 -0.23 0.817 -.7176194 .5673762
|
L.defier#m1 |
0 1 | .0497029 .2771395 0.18 0.858 -.5002019 .5996077
|
L2.defier#m2 |
0 1 | .4190932 .2787426 1.50 0.136 -.1339925 .9721789
|
L3.defier#m3 |
0 1 | 1.926908 .5189397 3.71 0.000 .8972196 2.956597
|
_cons | 6.062341 .4269764 14.20 0.000 5.215127 6.909555
--------------------+----------------------------------------------------------------
cty | absorbed (100 categories)
Code:
* areg doesn't drop variables if post 1999
# delimit ;
areg ${outcome}
1.intent#0.defier#1.m0
l1.1.intent#l1.0.defier#1.m1
l2.1.intent#l2.0.defier#1.m2
l3.1.intent#l3.0.defier#1.m3
c.running#0.defier#1.m0
l1.c.running#l1.0.defier#1.m1
l2.c.running#l2.0.defier#1.m2
l3.c.running#l3.0.defier#1.m3
0.defier#m0
0.l1.defier#m1
0.l2.defier#m2
0.l3.defier#m3
if inrange(year,2000,2006)
& insample
, cluster(cty) absorb(cty);
# delimit cr
Linear regression, absorbing indicators Number of obs = 700
F( 12, 99) = 6.15
Prob > F = 0.0000
R-squared = 0.6168
Adj R-squared = 0.5444
Root MSE = 1.1326
(Std. Err. adjusted for 100 clusters in cty)
-------------------------------------------------------------------------------------
| Robust
unemp_rate | Coef. Std. Err. t P>|t| [95% Conf. Interval]
--------------------+----------------------------------------------------------------
intent#defier#m0 |
1 0 1 | .4648492 .3104504 1.50 0.137 -.1511518 1.08085
|
L.intent#L.defier#|
m1 |
1 0 1 | -.0860283 .2276794 -0.38 0.706 -.5377936 .3657371
|
L2. |
intent#|
L2.defier#m2 |
1 0 1 | -.1695172 .3319051 -0.51 0.611 -.828089 .4890546
|
L3. |
intent#|
L3.defier#m3 |
1 0 1 | .226809 .4802937 0.47 0.638 -.7261979 1.179816
|
defier#m0#c.running |
0 1 | -.0109445 .0076418 -1.43 0.155 -.0261074 .0042184
|
L.defier#m1#|
cL.running |
0 1 | .0101195 .0064702 1.56 0.121 -.0027188 .0229577
|
L2.defier#m2#|
cL2.running |
0 1 | .0129306 .0083487 1.55 0.125 -.003635 .0294962
|
L3.defier#m3#|
cL3.running |
0 1 | -.0003409 .0113759 -0.03 0.976 -.0229131 .0222313
|
defier#m0 |
0 1 | -.5470303 .3260741 -1.68 0.097 -1.194032 .0999715
|
L.defier#m1 |
0 1 | -.4926713 .2377283 -2.07 0.041 -.9643758 -.0209669
|
L2.defier#m2 |
0 1 | -.0659969 .2842434 -0.23 0.817 -.6299975 .4980037
|
L3.defier#m3 |
0 1 | .023994 .2798196 0.09 0.932 -.5312288 .5792167
|
_cons | 6.464826 .2387412 27.08 0.000 5.991112 6.93854
--------------------+----------------------------------------------------------------
cty | absorbed (100 categories)
Code:
* reghdfe doesn't drop variables
# delimit ;
reghdfe ${outcome}
1.intent#0.defier#1.m0
l1.1.intent#l1.0.defier#1.m1
l2.1.intent#l2.0.defier#1.m2
l3.1.intent#l3.0.defier#1.m3
c.running#0.defier#1.m0
l1.c.running#l1.0.defier#1.m1
l2.c.running#l2.0.defier#1.m2
l3.c.running#l3.0.defier#1.m3
0.defier#m0
0.l1.defier#m1
0.l2.defier#m2
0.l3.defier#m3
if inrange(year,1996,2006)
& insample
, cluster(cty) absorb(cty);
# delimit cr
(converged in 1 iterations)
HDFE Linear regression Number of obs = 1,100
Absorbing 1 HDFE group F( 12, 99) = 22.41
Statistics robust to heteroskedasticity Prob > F = 0.0000
R-squared = 0.6201
Adj R-squared = 0.5774
Within R-sq. = 0.1373
Number of clusters (cty) = 100 Root MSE = 1.3204
(Std. Err. adjusted for 100 clusters in cty)
-------------------------------------------------------------------------------------
| Robust
unemp_rate | Coef. Std. Err. t P>|t| [95% Conf. Interval]
--------------------+----------------------------------------------------------------
intent#defier#m0 |
1 0 1 | .0634621 .2583711 0.25 0.806 -.4492021 .5761264
|
L.intent#L.defier#|
m1 |
1 0 1 | -.0027516 .1981444 -0.01 0.989 -.3959132 .3904099
|
L2. |
intent#|
L2.defier#m2 |
1 0 1 | -.2087028 .2232519 -0.93 0.352 -.651683 .2342773
|
L3. |
intent#|
L3.defier#m3 |
1 0 1 | .1684336 .2454237 0.69 0.494 -.3185403 .6554075
|
defier#m0#c.running |
0 1 | -.016822 .0066538 -2.53 0.013 -.0300245 -.0036195
|
L.defier#m1#|
cL.running |
0 1 | .0023001 .0047169 0.49 0.627 -.0070593 .0116596
|
L2.defier#m2#|
cL2.running |
0 1 | .0021448 .0048294 0.44 0.658 -.0074377 .0117274
|
L3.defier#m3#|
cL3.running |
0 1 | .0171726 .0048115 3.57 0.001 .0076255 .0267197
|
defier#m0 |
0 1 | -.2761202 .2393532 -1.15 0.251 -.7510489 .1988084
|
L.defier#m1 |
0 1 | -.453506 .149009 -3.04 0.003 -.7491721 -.1578398
|
L2.defier#m2 |
0 1 | -.0499182 .1498133 -0.33 0.740 -.3471802 .2473439
|
L3.defier#m3 |
0 1 | .8106551 .1642063 4.94 0.000 .4848342 1.136476
-------------------------------------------------------------------------------------
Absorbed degrees of freedom:
----------------------------------------------------------------------+
Absorbed FE | Num. Coefs. = Categories - Redundant |
--------------------+-------------------------------------------------|
cty | 0 100 100 * |
----------------------------------------------------------------------+
* = fixed effect nested within cluster; treated as redundant for DoF computation
Thank you.
0 Response to Collinearity issues with -areg- and not with -reghdfe-
Post a Comment