1. Goal is to describe the data, to estimate different models und to compare the estimates. Open hd-data and have a quick look at the data. Make sure that the data is sorted by person id and survey year.
Generate a new variable num as a running number within each person (use _n). Generate a second new variable N that summarizes the rows within each person (use _N). Move these new variables at top of the dataset (use move or order) and control the result und the data browser.
Label and summarize the new variables; the range of num is 1-14697, the range of N is 1-33.
2. It is not desirable to have single spells in our dataset. Drop all persons with only one observation. The number of cases deleted is 28.
Now the range of num is 1-14697, the range of N is 2-33.
Sort the data again by person id and survey year.
3. We want to analyse the transition to homeownership. Therefore, all persons in the dataset must not be homeowners at the beginning of the observation. Control this. The number of homeowners at the beginning of observation is 593.
Generate a new variable flag=1 for those who are homeowners in the first spell. Control the result; flag is 1 in ___________ cases.
Within persons, replace all following spells with 1 if the first spell is 1. Control the result also in the browser (use order flag num). flag is 1 in ___________ cases.
Drop all cases with flag==1. Sort the data again by person id and survey year.
The number of persons is now ____________. The number of all spells is ___________.
4. Describe the data based on the first or last observation for each person. For the ease of analyses, do not use weights.
How many persons are homeowners at the start of observation? ______________
How many persons are homeowners at the end of observation? ______________
5. We want to analyse at which age people become homeowners for the first time, based on the age of the household head (agehh). First, have a look at the age variable in the data browser (use order). Is it time variable (yes/no) _______? The age range in the data is _______________.
Declare the data as survival data with stset agehh, id(pid) failure(owner).
The number of subjects is _______________, the number of failures is _______________.
Why is the number of failures a bit different to the number of homeowners at the end of observation? _______________________________________________
_____________________________________________________________
6. Type stdescribe. The mean number of records is ____________________, the mean time at risk is ________________, the percentage of entries into homeownership is _________________.
7. Type sts list, failure by(class) compare at(20 25 30 35 40). Estimates of the Kaplan-Meier functions for the transition into first homeownership by age 40 are:
For skilled (blue-collar) workers: ___________, and for white collar workers: ___________.
8. Estimate a simple logit model, and estimate the same model with robust standard errors, by typing the option after the comma:
logit owner agehh agesq sex labgro i.partner kidnr powner
logit owner agehh agesq sex labgro i.partner kidnr powner, vce(cluster pid)
What is the difference? ____________________________________________________
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