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From |
Marc Peters <marcpeters32c@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Splines |

Date |
Thu, 21 Feb 2013 09:53:25 -0600 |

Dear Nick, Thank you for these clarifications. Best, Marc On Thu, Feb 21, 2013 at 3:19 AM, Nick Cox <njcoxstata@gmail.com> wrote: > Thanks for filling out the details. I've not read that paper. But in > any case I don't know what you mean by "dealing with temporal > dependence". Dependence in time series can mean anything from > > dependence in error structure which is regarded as a nuisance or > complication in regression-type models > > to > > dependence treated as the main feature by some kind of time series > modelling, such as binary time series modelling or Markov chains. > > It seems, however, that what you have in mind something else roughly > in between those extremes. > > It seems that this is most likely to be carried forward by people > familiar with the literature now identified. Alternatively, if this is > a widely used method, there should be guides somewhere on how to do it > in Stata. > > Nick > > On Thu, Feb 21, 2013 at 2:16 AM, Marc Peters <marcpeters32c@gmail.com> wrote: >> 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 > > 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. > > 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. > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/statalist-faq/ > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Splines***From:*Marc Peters <marcpeters32c@gmail.com>

**Re: st: Splines***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: Splines***From:*Marc Peters <marcpeters32c@gmail.com>

**Re: st: Splines***From:*Nick Cox <njcoxstata@gmail.com>

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