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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 00:50:04 -0400
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/