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From |
Robert A Yaffee <bob.yaffee@nyu.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Binary time series |

Date |
Wed, 22 Sep 2010 00:18:12 -0400 |

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 > > *************************************************************** > > > > > > > > * > > * 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/

**References**:**Re: st: Binary time series***From:*Robert A Yaffee <bob.yaffee@nyu.edu>

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