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Re: st: Splines


From   Marc Peters <marcpeters32c@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Splines
Date   Wed, 20 Feb 2013 20:16:24 -0600

Dear Nick,

Thank you for your prompt answer. I am very sorry for being imprecise.


The reference I am talking about is Beck, Nathaniel; Jonathan N. Katz
and Richard Tucker. 1998. "Taking Time Seriously: Time-Series
Cross-Section Analysis with a Binary Dependent Variable." American
Journal of Political Science, 42(4) 1260-1288.


BTSCS is the word they use for Time-Series Cross-Section Analysis with
a Binary Dependent Variable. In their article they replicate a study
of militarized conflict, where a country dyad do or do not have a
conflict in a given year. As a conflict can persist for a number of
consecutive years, the data structure is quite similar to mine. Your
point about lowess is well taken, but if I understand you correctly
you would not recommend using splines for any analyses with repeated
events? Would you recommend another strategy for dealing with temporal
dependence. As I have understood it, a lagged dependent variable is
insufficient.


Once again, thank you for your help


Marc

On Wed, Feb 20, 2013 at 7:28 PM, Nick Cox <njcoxstata@gmail.com> wrote:
> You were asked to read the FAQ before posting. That explains that you
> are asked not to give minimal name (date) references. Also, BTSCS
> looks to me like jargon from your field. It is difficult not to use
> jargon on a list like this, but unexplained jargon nevertheless cuts
> down the number of people who might both read and reply to your posts.
>
> In terms of your question, running -lowess- and calling the smooth a
> spline does not make it a spline. There are many classes of spline,
> but I doubt that there's any definition that generous.
>
> The most common kinds of splines are linear and cubic. -mkspline-
> creates either kind. My best advice is to read the manual entry on
> -mkspline- and run through the examples in the help.
>
> I can't easily follow what you are trying to do otherwise. If you are
> saying that your response (dependent variable, in your terms) flips
> between states of 0 and states of 1, it sounds quite unsuitable for
> splines. But you seem to be trying to model it as a function of
> duration, not time; sorry, but you lost on me on that.
>
> My bottom line is that -lowess- is _not_ a spline method.
>
> Nick
>
> On Thu, Feb 21, 2013 at 1:08 AM, Marc Peters <marcpeters32c@gmail.com> wrote:
>
>> I have never used splines before and have a rather silly question. I
>> am running a BTSCS model and have read up on my Beck, Katz and Tucker
>> (1998) and understood that I should use either temporal dummies or
>> splines to adjust for temporal dependence.
>>
>> The data is structured as duration data, with events coded as 1 and
>> non-events as 0. The dependent variable is measured at discrete
>> intervals (years) and an event can go on for several years (it often
>> does).
>>
>> From the data I have created a variable (duration) counting the number
>> of years since the last event. The variable is coded as 0 as long as
>> the event is ongoing.
>>
>> From this variable I create lowess splines using
>>
>> lowess Y duration, gen (spline)
>>
>> and then:
>>
>>
>> logit Y X spline, cluster(id)
>>
>>
>> I have understood that this is what you are supposed to do, but since
>> the spline is defined on the dependent variable the spline variable
>> always take on a high value when duration=0 (i.e. there is an event).
>> Consequently, when running the model I receive the following message
>> when running the command:
>>
>>
>> spline > .4679623 predicts data perfectly
>>
>>
>> I would be very grateful if anyone could help me with what it is I am
>> doing wrong. In the end, I should probably use cubic splines but first
>> I want to understand the simple principle.
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