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Re: st: count data truncated at one
From
Laurie Molina <[email protected]>
To
[email protected]
Subject
Re: st: count data truncated at one
Date
Mon, 11 Jun 2012 20:01:41 -0500
Nick,
Thanks for your repply.
Yes, there are structural reasons why only those responses are possible.
People included in the regression are members of a group defined as
people paying 2 to ten times a reference number.
I was thinking in ologit, but as there is cardinality involved, I was
looking for a method that would consider all the available
information, that is a method that would consider both the cardinal
and ordinal properties of my data.
I was thinking on reescaling the dataset so that 2 becomes 0, 3
becomes 1, and so on. I know that this would not solver the high
frequency of 10's (8's after reescaling), but I think my coefficients
will still consistently estimate population parameters, as maximum
likelihood estimation with poisson is robust to incorrer
especification of the distribution as long as the conditional
expectation function is correctly specified...
Would it be terrible to do such a reescalation?
Thank you again!
On Mon, Jun 11, 2012 at 7:40 PM, Nick Cox <[email protected]> wrote:
> Are there structural reasons why only those responses are possible?
> What about -ologit-?
>
> Nick
>
> On Tue, Jun 12, 2012 at 1:33 AM, Laurie Molina <[email protected]> wrote:
>
>> I want to model a dependent variable , say y, taking values on the set
>> {2,3,4,...,10}
>> Most of the observations have y=2, then the frequencies are decreasing
>> for higher values of y, but then when there is also a high frequency
>> of observations with y=10.
>> First i tought of using count data models, but i don't know if there
>> is anything like a "One truncated Poisson" similar to the "Zero
>> truncated Poisson" model...
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