b. The slope coefficient estimates cannot measure changes in the predicted probability of Y=1 c. The estimators can be asymptotically normally distributed d. All of the above b. Its expected value equals the ratio of the probability of Y=0 to the probability of Y=1 c. Its variance equals the product of the probability of Y=0 and the probability of Y=1 d. All of the above c. The misclassification of key dummy explanatory variables due to measurement error d. non-e of the above 10. Refer to the model and estimates in the previous question.
You want to test that ceteris paribus, men and women have the same probability of earning >$500,000. Under the null, the Wald test statistic is asymptotically chi-squared distributed with a. 1 degree of freedom SOLUTIONS Young PART B. i. (8 marks) Discuss the effects of PHI on the probability of visiting a GP and compare these effects for the two subsamples of young and old women. Repeat the exercise for the KIDS variable.
Do you think that these variables are likely to violate the zero conditional mean assumption? Discuss The sign is as expected since PHI makes it cheaper to use GP services and women who expect to visit GPs more often are more likely to purchase PHI. The latter implies that ZCM may be violated due to a selection effect. KIDS: In the young subsample, the coefficient on KIDS is positive and statistically different from zero at the 10% level (t statistic = 1.874 >1 . 645); the possibility of DOCTOR visit is usually higher for those with based mostly children. In the old subsample, the signal of the agent indicates the effect is negative however the coefficient can be statistically insignificant from zero at typical levels (t stat = 1 . 276< 1.645).
A priori, the expected sign is ambiguous; women may visit GPs for children’s medical care as well as their own (positive) but at the same time they may become busier due to child rearing (negative). For the old sample, KIDS may be older and hence mothers no longer visit GPs for the children’s health. Other reasonable explanations are acceptable. You can argue both ways on the ZCM assumption: for example, you can argue that fertility decisions are exogenous to GP visits.
You could also argue that there is an omitted variable bias (KIDS is picking up some unobserved component e.g. better health measurement than what is being captured by the existing explanatory variables). Also if the true underlying relationship depends on the number of resident dependent children, KIDS is top-coded at 1, causing the ZCM assumption to fail due to a measurement error correlated with this variable. Additional material: You could also earn marks (lost elsewhere in the question) by discussing the size of the effects.
For example, the effect among the young seems nontrivial in the sense that the coefficient’s magnitude is slightly over 40% of that of the coefficient on the poor health indicator (HEALTH) while for the old, the variable seems far less economically relevant relative to HEALTH. ii. (5 marks) If there is equity of access then variables related to income, education and private health insurance should not affect visits to GPs. When the models are re-estimated without these variables (i.e. with only AGE, HEALTH and KIDS included) the log-likelihood values are 937.92 for the young sub-sample and 898.63 for the old. Using these results evaluate the null hypothesis of equity of access. Since LLROLD >10.
0705, we all reject the null in the 5% significance level in the old subsample and consider that there is some evidence against equity of access among the list of old women. iii. (4 marks) Consider two types of women: type #1 where GROW OLDER = twenty, HEALTH sama dengan 1, CASH FLOW = 20 and all other variables sama dengan 0; type #2 is usually identical except that AGE sama dengan 60. Take note of the equation(s) you would use for compare the probability of visiting a GP for the two types of women. Using the probit results can you determine which in turn of these two types of women are more likely to have frequented a DOCTOR in the last 2 weeks?
If your answer is yes then associated with comparison, in case your answer is no then make clear what information you would need to make the evaluation. Another possible answer should be to write down the regular CDF to get the two types and believe the equation for type 2 will probably be greater than type Likely complications (one in the following yet another sensible problem): -The potential selection tendency which comes up when the decision to report zero cash flow or decline reporting any kind of is linked to the decision to work with GP solutions.
For immediate, top cash flow groups may be more jealous of their salary information as well as more likely to always be health conscious and visit Gps navigation in consequence; not including the said individuals will affect all coefficient quotes as the model would have to predict a lower probability of GP visit on average. -The decrease in the sample size and the producing increase in common errors. The incomplete cases may possibly still provide useful information about the effects of various other variables on GP trips and the investigator has thrown away this information. The first step.
Calculate a predicted probability for each person in the relevant subsample. Step two. Obtain a predicted binary end result for each person using a category rule: if person i’s predicted probability exceeds c, the believed outcome is definitely 1 and otherwise 0. It is fine if you use 0. 5 and also the sample suggest.
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