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Re: FW: st: Regression with multiple age groups
David Greenberg <email@example.com>
Re: FW: st: Regression with multiple age groups
Wed, 25 Apr 2012 13:18:08 -0400
IF you have 21 years of annual observations, this is very few
observations for ARIMA modeling. To identify a model with confidence
you would want many more observations than that. A model for count
data, such as Poisson or negative binomial regression might be your
best option. However, you will only be able to enter a limited number
of predictors given the small number of observations available to you.
David Greenberg, Sociology Department, New York University
On Wed, Apr 25, 2012 at 11:21 AM, Shirley Sy <firstname.lastname@example.org> wrote:
> Hi David,
> Thanks for your reply!
> I took the data from the Office of National Statistics website for the years 1980 to 2000. My independent variable is the divorce rate (which I calculated myself using the total number of divorces in a given year divided by the total number of marriages in the same year) and my explanatory variables are: husband's age at divorce, wife's age at divorce, husband's previous marital status, wife's previous marital status, combination of husband's and wife's previous marital status (i.e. first marriage for both, one party previously divorced, both previously divorced), duration of marriage (under 2yrs, 2-5, 6-9, 10-14, 15-19, 20-24, 25-29, 30+, not stated), average number of children per couple, female unemployment rate and male unemployment rate.
> I was planning to do OLS and have not considered poisson or negative binomial as of yet. Unfortunately I was given this project to do without any supervision and absolute minimal help and I was only taught the very basics of Stata a year ago so I didn't intend on doing other models with the fear of doing it completely wrong. Would an ARIMA model be appropriate for this data? Shirley
>> Date: Wed, 25 Apr 2012 09:09:33 -0400
>> Subject: Re: st: Regression with multiple age groups
>> From: email@example.com
>> To: firstname.lastname@example.org
>> Dear Shirley,
>> Others will agree that you need to tell the list more about the data
>> and the analysis that you intend to do, before we can make useful
>> It would be appropriate to handle the age categories, at least
>> initially, by using a separate dummy variable for each category except
>> the first (which will be fitted by the intercept term in your model).
>> You can then plot the coefficients against the midpoint of the
>> category and consider whether to revise the model.
>> You said that you have the age category, separately, for the husband
>> and the wife. Thus, you would use two separate sets of dummy
>> variables, one for the husband's age and the other for the wife's age.
>> You may want to explore the possibility of interactions between the
>> two ages.
>> Please say more about the counts that you listed for 1985 and 1986 (I
>> assume that those are two of the 20 years). They appear to be the
>> frequency distribution of the numbers of divorces by one of the sets
>> of age categories. If you are projecting divorce rates, and those
>> counts represent the number of divorces in one year, what is the
>> denominator for the rate? Do you have the denominator for each age
>> category (and even the combination of husband's age category and
>> wife's age category) or only for the year as a whole?
>> Do the data come from a survey? If so, you will need to take the
>> sampling design and the weights into account.
>> What type of regression are you planning to use? Ordinary regression
>> will probably not be appropriate for rates. You should consider
>> Poisson regression (and perhaps negative binomial regression)?
>> So far, I can envision a model that contains a time pattern (initially
>> based on a dummy variable for each year after the first), effects for
>> husband's age, and effects for wife's age (and perhaps some form of
>> husband-wife interaction). Do you have covariates that you are
>> planning to include?
>> I look forward to seeing more information.
>> David Hoaglin
>> On Wed, Apr 25, 2012 at 12:36 AM, Shirley Sy <email@example.com> wrote:
>> > Dear Statalisters,
>> > I am a complete beginner at Stata so my question is very basic but I am having trouble finding an answer on the web. I am doing a time series regression project forecasting divorce rates. My data spans 20 years and for both husband and wife the 'age at divorce' variable is split into groups i.e it looks something like this:
>> > Year under 20 20 to 29 30 to 39 40 to 49 50 to 59 60plus not stated1985 458 1154 78 52 3 2 3
>> > 1986 221 956 50 59 9 5 0
>> > How would I run a regression with the total numbers in each age group? Would I use dummy variables? I understand how I would do it if I had individual ages but since this is a time series model and I have the total number in each age group, I am finding it slightly more complicated.
>> > ThanksShirley
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