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Re: st: RE: RE: Binary time series
From
Robert A Yaffee <[email protected]>
To
[email protected]
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 <[email protected]>
Date: Thursday, September 30, 2010 0:52 am
Subject: Re: st: RE: RE: Binary time series
To: [email protected]
> 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 <[email protected]>
> Date: Wednesday, September 29, 2010 7:29 pm
> Subject: st: RE: RE: Binary time series
> To: [email protected]
>
>
> > 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: [email protected]
> > ***************************************************************
> >
> > -----Original Message-----
> > From: [email protected]
> > [mailto:[email protected]] On Behalf Of Nick Cox
> > Sent: Thursday, 23 September 2010 12:45 AM
> > To: '[email protected]'
> > 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
> > [email protected]
> >
> > 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 <[email protected]>
> Date: Wednesday, September 29, 2010 7:29 pm
> Subject: st: RE: RE: Binary time series
> To: [email protected]
>
>
> > 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: [email protected]
> > ***************************************************************
> >
> > -----Original Message-----
> > From: [email protected]
> > [mailto:[email protected]] On Behalf Of Nick Cox
> > Sent: Thursday, 23 September 2010 12:45 AM
> > To: '[email protected]'
> > 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
> > [email protected]
> >
> > 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/