Re: st: Binary time series

[Robert A Yaffee <bob.yaffee@nyu.edu>]

John,
  Just another thought.   You might want to consider using gllamm or xtmelogistic.
   Robert


Robert A. Yaffee, Ph.D.
Research Professor
Silver School of Social Work
New York University

Biosketch: http://homepages.nyu.edu/~ray1/Biosketch2009.pdf

CV:  http://homepages.nyu.edu/~ray1/vita.pdf

----- Original Message -----
From: Robert A Yaffee <bob.yaffee@nyu.edu>
Date: Tuesday, September 21, 2010 11:23 pm
Subject: Re: st: Binary time series
To: statalist@hsphsun2.harvard.edu


> John,
>       Irregularly spaced time series has been handled by attempts to 
> model intermittend demand.
> Croston's method and variations on it have been used to handle such 
> data.  Unfortunately, Stata has no command for Croston's method.   In 
> high frequency financial data,  they attempt to model integrated 
> volatility or realized volatility by taking the absolute or squared 
> value of transactions at various time intervals.  Sometimes the 
> interval is a five or ten minute interval.  Sometimes the interval is 
> the complete workday, during which the value of the transactions might 
> be summed.
> The problem with the square is that jumps in volatility may occur that 
> can complicate the assessment of volatility if they are not taken into 
> account.  One thing to consider is how to decide upon the proper 
> interval in which a variance can be computed.   
>     Croston's method uses a simple exponential smoother to handle the 
> interval between observations.   A different exponential smoother used 
> to account for the magnitude of the observation. 
>     Finally, to achieve the mean demand rate, Croston divides the 
> magnitude of demand by the interval time, each of which are the 
> dependent variables in the simple exponential smoother.
>     There have been modifications of this method by Johnston and 
> Boylan and Boylan and Syntetos somewhat later.
>      Siem Jan Koopman with others has set forth a generalized scoring 
> algorithm handling such time series models.  But I haven't seen the 
> software yet.
>      These may give you some ideas.
>          Cheers,
>                 Robert
> 
>    
>        
>        
> 
> Robert A. Yaffee, Ph.D.
> Research Professor
> Silver School of Social Work
> New York University
> 
> Biosketch: http://homepages.nyu.edu/~ray1/Biosketch2009.pdf
> 
> CV:  http://homepages.nyu.edu/~ray1/vita.pdf
> 
> ----- Original Message -----
> From: John Morton <john.morton@optusnet.com.au>
> Date: Tuesday, September 21, 2010 8:14 pm
> Subject: st: Binary time series
> To: statalist@hsphsun2.harvard.edu
> 
> 
> > Hi again,
> > 
> > Same message as 90 minutes ago, this time with a subject heading. My
> > apologies for overlooking this in the previous post.
> > 
> > 
> > I am seeking advice on analysis of a time series dataset in Stata. 
> The 
> > same
> > site was visited irregularly 30 times over 3 years (median interval 
> between
> > visits 35 days, range 18 to 68 days). At each visit, usually 5 
> > tadpoles (but
> > sometimes 6 or 9) were sampled (numbers were limited because this is 
> an
> > endangered species). Different tadpoles were sampled at each visit. 
> Each
> > tadpole was tested and categorised as test positive or test negative.
> > Apparent prevalences were 1.00 at about half of the visits and 0.00 
> at 
> > about
> > 25% of visits. 
> > 
> > The researcher?s question is whether prevalence varies by month (ie 
> Jan,
> > Feb, Mar etc) or by season. 
> > 
> > The features of this data that seem important are that the errors 
> > would be
> > expected to be serially correlation over time, the dependent 
> variable 
> > is
> > binary, prevalences of 0 and 1 were common, the very small number of
> > tadpoles sampled at each visit, and these are not panel data (ie different
> > tadpoles were sampled at each visit).
> > 
> > I have done some exploratory modelling treating prevalence as a continuous
> > dependent variable (using -regress-) after declaring the data to be
> > time-series data (with sequential visit number rather than day 
> number 
> > as the
> > time variable, using -tsset-). With a null model, tests for serial
> > correlation (Durbin-Watson test (-estat dwatson-), Durbin?s 
> > alternative (h)
> > test (-estat durbinalt-),Breush-Godfrey test ( -estat bgodfrey,lag(6)-),
> > Portmaneau (Q) test (-wntestq-) and the autocorrelogram (-ac-)(all 
> > from Baum
> > 2006) indicate serial correlation. In contrast, after fitting month 
> as 
> > a
> > fixed effect, these tests do not support rejecting the null 
> hypothesis 
> > that
> > no serial correlation exists. However treating prevalence (a 
> > proportion) as
> > a continuous dependent variable (using -regress-) is inappropriate. 
> 
> > 
> > Any suggestions on approaches to answer the research question would 
> be 
> > much
> > appreciated.
> > 
> > Many thanks for any help.
> > 
> > John
> > 
> > ***************************************************************
> > Dr John Morton BVSc (Hons) PhD MACVSc (Veterinary Epidemiology)
> > Veterinary Epidemiological Consultant
> > Jemora Pty Ltd
> > PO Box 2277
> > Geelong 3220
> > Victoria Australia
> > Ph:  +61 (0)3 52 982 082
> > Mob: 0407 092 558
> > Email: john.morton@optusnet.com.au
> > ***************************************************************
> > 
> > 
> > 
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