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Re: st: RE: RE: Binary time series


From   Robert A Yaffee <bob.yaffee@nyu.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: RE: RE: Binary time series
Date   Thu, 30 Sep 2010 00:59:14 -0400

The R packages referred to are its and zoo.

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: Thursday, September 30, 2010 0:52 am
Subject: Re: st: RE: RE: Binary time series
To: statalist@hsphsun2.harvard.edu


> Dear Nick, 
>     My references to common practices and methods in the fields of 
> intermittent demand analysis and financial econometrics rather than 
> referring only to a particular paper.  They were based more on memory 
> than particular citations.  I was offering leads not citations while 
> packing for a quick departure for a National Science Foundation 
> meeting.  However if you would like evidence of this, you can google  
> intermittent demand, Croston's method or realized and/or integrated 
> volatility in the fields of irregularly spaced time series or 
> intermittent demand to see for yourself.
>     When such things are commonplace among practitioners, it is not 
> necessary to cite them.
>        Cheers,
>            Robert
> 
> 
> 
> 
> Stochastic models underlying
> Croston’s method for
> intermittent demand forecasting(2005)
> by Lydia Shenstone and Rob Hyndman  
> (using R)
> FOUND AT
> http://robjhyndman.com/papers/croston.pdf
> 
> 
> ISF 2002 –23rdto 26thJune 2002
> Forecasting, Ordering and Stock-Holding for Erratic Demand
> by 
> Andrew Eaves
> Lancaster University /
> Andalus Solutions Limited
> 
> 
> The R package called its also has it.  Published: 2009-09-06
> Author: 	Portfolio & Risk Advisory Group, Commerzbank Securities
> Maintainer: 	Whit Armstrong <armstrong.whit at gmail.com>
> 
> 
> As for the use of references to integrated volatility or realized 
> volatility in irregularly spaced time series, this too is common among 
> practitioners of high frequency volatility analysis, about which many 
> papers have been written---too many to cite here.  My reference was to 
> a method not a particular paper there. But you can also google this 
> topic if you need evidence of it.
> 
>     Cheers,
>           Bob
> 
> 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: Wednesday, September 29, 2010 7:29 pm
> Subject: st: RE: RE: Binary time series
> To: statalist@hsphsun2.harvard.edu
> 
> 
> > Many thanks to Robert (Yaffee) and Nick (Cox) for their excellent
> > suggestions on approaches to analysis of the binary time series data 
> I
> > described. I now have plenty to look into and think about.
> > 
> > Nick, 'Baum 2006' is Baum CF (2006) An Introduction to Modern Econometrics
> > Using Stata, Stata Press, College Station. Apologies for not 
> including 
> > these
> > details in my original posting.
> > 
> > 
> > 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
> > ***************************************************************
> > 
> > -----Original Message-----
> > From: owner-statalist@hsphsun2.harvard.edu
> > [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Nick Cox
> > Sent: Thursday, 23 September 2010 12:45 AM
> > To: 'statalist@hsphsun2.harvard.edu'
> > Subject: st: RE: Binary time series
> > 
> > Bob Yaffee did allude to some of the literature on irregular time series,
> > and there's plenty more. For example, astronomers and others have a 
> separate
> > literature on getting spectra out of irregular series. 
> > 
> > But if this were my problem I wouldn't go that way. I've a gut 
> feeling 
> > that
> > a simple regression-like model could work quite well for 30 data 
> > points but
> > less well for any time series model you care to name. Time series models
> > seem more data-hungry even when they work. 
> > 
> > The researcher's question appears to hinge on looking at 
> seasonality. 
> > Month
> > as such I imagine to be quite arbitrary and artificial for tadpoles 
> (unless
> > lunar cycles are important, and if they are, you would be modelling 
> them
> > directly). Also, if you have a parameter per month, you are 
> spreading 
> > the
> > information pretty thinly. 
> > 
> > I would work with Fourier series picking up dependence on time of 
> year 
> > and
> > then check for error structure. There is Stata-based literature at 
> > 
> > SJ-6-4  st0116  . . . .  Speaking Stata: In praise of trigonometric
> > predictors
> >         . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  
> 
> > N. J.
> > Cox
> >         Q4/06   SJ 6(4):561--579                                 (no
> > commands)
> >         discusses the use of sine and cosine as predictors in
> >         modeling periodic time series and other kinds of periodic
> >         responses
> > 
> > SJ-6-3  gr0025  . . . . . . . . . . . . Speaking Stata: Graphs for all
> > seasons
> >         (help cycleplot, sliceplot if installed)  . . . . . . . . .  
> 
> > N. J.
> > Cox
> >         Q3/06   SJ 6(3):397--419
> >         illustrates producing graphs showing time-series seasonality
> > 
> > which may help in one way or another. Both papers are accessible via 
> the
> > Stata Journal. 
> > 
> > You have a response that is a proportion. See for a review  
> > 
> > SJ-8-2  st0147  . . . . . . . . . . . . . . Stata tip 63: Modeling
> > proportions
> >         . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 
> C. 
> > F.
> > Baum
> >         Q2/08   SJ 8(2):299--303                                 (no
> > commands)
> >         tip on how to model a response variable that appears
> >         as a proportion or fraction
> > 
> > In addition, converting time of year to a circular scale might help. 
> There
> > is a bundle of circular statistics programs in -circular- on SSC. 
> > 
> > At home we have tadpoles sometimes in a small pond in our garden, 
> but 
> > I have
> > no data to share. 
> > 
> > I don't know what Baum 2006 is. (But then Bob Yaffee didn't even 
> give 
> > years
> > in his "references"....) 
> > 
> > Nick 
> > n.j.cox@durham.ac.uk 
> > 
> > John Morton
> > 
> > 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. 
> 
> > 
> > 
> > *
> > *   For searches and help try:
> > *   http://www.stata.com/help.cgi?search
> > *   http://www.stata.com/support/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/statalist/faq
> > *   http://www.ats.ucla.edu/stat/stata/
> 
> 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: Wednesday, September 29, 2010 7:29 pm
> Subject: st: RE: RE: Binary time series
> To: statalist@hsphsun2.harvard.edu
> 
> 
> > Many thanks to Robert (Yaffee) and Nick (Cox) for their excellent
> > suggestions on approaches to analysis of the binary time series data 
> I
> > described. I now have plenty to look into and think about.
> > 
> > Nick, 'Baum 2006' is Baum CF (2006) An Introduction to Modern Econometrics
> > Using Stata, Stata Press, College Station. Apologies for not 
> including 
> > these
> > details in my original posting.
> > 
> > 
> > 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
> > ***************************************************************
> > 
> > -----Original Message-----
> > From: owner-statalist@hsphsun2.harvard.edu
> > [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Nick Cox
> > Sent: Thursday, 23 September 2010 12:45 AM
> > To: 'statalist@hsphsun2.harvard.edu'
> > Subject: st: RE: Binary time series
> > 
> > Bob Yaffee did allude to some of the literature on irregular time series,
> > and there's plenty more. For example, astronomers and others have a 
> separate
> > literature on getting spectra out of irregular series. 
> > 
> > But if this were my problem I wouldn't go that way. I've a gut 
> feeling 
> > that
> > a simple regression-like model could work quite well for 30 data 
> > points but
> > less well for any time series model you care to name. Time series models
> > seem more data-hungry even when they work. 
> > 
> > The researcher's question appears to hinge on looking at 
> seasonality. 
> > Month
> > as such I imagine to be quite arbitrary and artificial for tadpoles 
> (unless
> > lunar cycles are important, and if they are, you would be modelling 
> them
> > directly). Also, if you have a parameter per month, you are 
> spreading 
> > the
> > information pretty thinly. 
> > 
> > I would work with Fourier series picking up dependence on time of 
> year 
> > and
> > then check for error structure. There is Stata-based literature at 
> > 
> > SJ-6-4  st0116  . . . .  Speaking Stata: In praise of trigonometric
> > predictors
> >         . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  
> 
> > N. J.
> > Cox
> >         Q4/06   SJ 6(4):561--579                                 (no
> > commands)
> >         discusses the use of sine and cosine as predictors in
> >         modeling periodic time series and other kinds of periodic
> >         responses
> > 
> > SJ-6-3  gr0025  . . . . . . . . . . . . Speaking Stata: Graphs for all
> > seasons
> >         (help cycleplot, sliceplot if installed)  . . . . . . . . .  
> 
> > N. J.
> > Cox
> >         Q3/06   SJ 6(3):397--419
> >         illustrates producing graphs showing time-series seasonality
> > 
> > which may help in one way or another. Both papers are accessible via 
> the
> > Stata Journal. 
> > 
> > You have a response that is a proportion. See for a review  
> > 
> > SJ-8-2  st0147  . . . . . . . . . . . . . . Stata tip 63: Modeling
> > proportions
> >         . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 
> C. 
> > F.
> > Baum
> >         Q2/08   SJ 8(2):299--303                                 (no
> > commands)
> >         tip on how to model a response variable that appears
> >         as a proportion or fraction
> > 
> > In addition, converting time of year to a circular scale might help. 
> There
> > is a bundle of circular statistics programs in -circular- on SSC. 
> > 
> > At home we have tadpoles sometimes in a small pond in our garden, 
> but 
> > I have
> > no data to share. 
> > 
> > I don't know what Baum 2006 is. (But then Bob Yaffee didn't even 
> give 
> > years
> > in his "references"....) 
> > 
> > Nick 
> > n.j.cox@durham.ac.uk 
> > 
> > John Morton
> > 
> > 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. 
> 
> > 
> > 
> > *
> > *   For searches and help try:
> > *   http://www.stata.com/help.cgi?search
> > *   http://www.stata.com/support/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/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/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/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/


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