I ran a logistic regression with a cluster option. In one model, the
results showed a Wald Chi-Square in the order of 100,000. When I ran a
different model (by adding additional independent variables), I got a
much smaller Wald Chi-Square (in the order of 30,000 or 2,000 depending
on the additional independent variable being added). I have seen a
paper reporting a Wald Chi-Square as high as 30,000 in a logistic
regression with a robust option, but haven't been able to locate any
information about why I got such a high Wald Chi-Square. Could someone
explain if my results are normal or if I have done something wrong?
Below is the partial output for three models. The three models are
specified as follows:
Model 1: y is a function of 23 x's (call them x1 - x23)
Model 2: y is a function of x1 - x23 in model 1 plus x24
Model 3: y is a function of x1 - x23 in model 1 plus x25
Thanks.
Daniel Indro
---Model 1---
Iteration 0: log pseudolikelihood = -1413.5696
Iteration 1: log pseudolikelihood = -1348.5214
Iteration 2: log pseudolikelihood = -1313.6178
Iteration 3: log pseudolikelihood = -1302.8919
Iteration 4: log pseudolikelihood = -1301.6519
Iteration 5: log pseudolikelihood = -1301.6301
Iteration 6: log pseudolikelihood = -1301.6301
Logit estimates Number of obs =
11309
Wald chi2(23) =
113167.68
Prob > chi2 =
0.0000
Log pseudolikelihood = -1301.6301 Pseudo R2 =
0.0792
(standard errors adjusted for clustering on
compid)
---Model 2---
Iteration 0: log pseudolikelihood = -1413.5696
Iteration 1: log pseudolikelihood = -1258.636
Iteration 2: log pseudolikelihood = -1226.6914
Iteration 3: log pseudolikelihood = -1220.7719
Iteration 4: log pseudolikelihood = -1219.9377
Iteration 5: log pseudolikelihood = -1219.9183
Iteration 6: log pseudolikelihood = -1219.9183
Logit estimates Number of obs =
11309
Wald chi2(24) =
32637.80
Prob > chi2 =
0.0000
Log pseudolikelihood = -1219.9183 Pseudo R2 =
0.1370
(standard errors adjusted for clustering on
compid)
---Model 3---
Iteration 0: log pseudolikelihood = -1413.5696
Iteration 1: log pseudolikelihood = -1386.0868
Iteration 2: log pseudolikelihood = -1299.8042
Iteration 3: log pseudolikelihood = -1288.04
Iteration 4: log pseudolikelihood = -1286.8147
Iteration 5: log pseudolikelihood = -1286.7925
Iteration 6: log pseudolikelihood = -1286.7925
Logit estimates Number of obs =
11309
Wald chi2(24) =
1954.11
Prob > chi2 =
0.0000
Log pseudolikelihood = -1286.7925 Pseudo R2 =
0.0897
(standard errors adjusted for clustering on
compid)
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