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Re: st: univariate time series intervention analysis


From   Robert A Yaffee <bob.yaffee@nyu.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: univariate time series intervention analysis
Date   Thu, 20 Jul 2006 23:11:30 -0400

you need
to convert the commas in the data to periods.
  A Dickey-Fuller test indicates that your series is stationary.  
Therefore, transformation to stationarity may not be needed.  You 
construct an intervention dummy-named Interv--equal to zero up to time 
22 and equal to 1 from 22 on.
  You could then try to test for autocorrelation--corrgram would 
suffice. 
But correlogram analysis would not reveal any significant 
autocorrelation or moving average innovations. Hence, you would run an 

arima y Interv, arima(0,0,0)

  You would not find that the interv dummy is statistically 
significant with these data, whether you request robust or nonrobust 
standard errors.

   Regards,
    Robert Yaffee
  




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

home address:
Apt 19-W
2100 Linwood Ave.
Fort Lee, NJ
07024-3171
Phone: 201-242-3824
Fax: 201-242-3825
yaffee@nyu.edu

----- Original Message -----
From: keto04 <keto04@skynet.be>
Date: Thursday, July 20, 2006 10:57 am
Subject: st: univariate time series intervention analysis

> Hello, I already posted this but maybe someone who can help missed 
> it. 
> Hope you do not mind that I try again;
> 
> does anyone have some experience with time series and
> intervention analysis?  I have the following yearly time series:
> 
> 0,661458
> 
> 0,738889
> 
> 0,766667
> 
> 0,638889
> 
> 0,727778
> 
> 0,688889
> 
> 0,611111
> 
> 0,722222
> 
> 0,661111
> 
> 0,711111
> 
> 0,744444
> 
> 0,672222
> 
> 0,633333
> 
> 0,666667
> 
> 0,777778
> 
> 0,722222
> 
> 0,755556
> 
> 0,727778
> 
> 0,744444
> 
> 0,777778
> 
> 0,727778
> 
> 0,627778
> 
> 0,661111
> 
> 0,683333
> 
> 0,694444
> 
> 0,805556
> 
> 0,7
> 
> 0,75
> 
> 0,783333
> 
> 0,805556
> 
> 
> This is for one country.  (i have also data for other countries, also
> between 0 and 1)
> I want to find out whether significant changes have occured over 
time.
> Especially the evolution since time index 22 (when using a time
> variable, going from 1 to 30). I want to find whether the series has
> increased linearly after this season.  How do I do this?  I 
> thought I
> start with Phillips Perron test for stationarity: there I find
> stationarity (I just use the: 'statistics', 'time series', 'test', 
> 'pp',include trend+default lags.  I get z(rho) and z(t) larger 
> than the
> critical values so stationarity. So no differences necessary I 
> thought.If I look at the autocorrelations (also via clicking) than 
> the auto and
> partial corr give four peaks but if I do same in SPSS, they fall 
> betweenthe confidence intervals so I thought then no need of AR or 
> MA.  If  I
> do a  PPplot  to test normality in SPSS (I can not do so in STATA 
> sinceget remark, even with simple graph: system limit exceeded, 
> and many
> green notations but no graph) all are nicely close to the 45°line.
> So I thought I just need to estimate:
> xt = w/(1-B)It+Nt  following the book of Mills.  With xt my time 
> seriesfrom above, It a dummy with zero for t = 1-21 and  1 for 
> t=22-30.  Nt is
> just the errorterm since no found ARMA.  Now how do I this
> practically??? I thought rewritten this model means xt(1-B)=wIt + Nt
> (1-B), so diff xt= wIt + diff Nt.  But do I this as follows:
> 'statistics', 'time series', 'ARIMA', choose d=1 and ma=1, 
> dependent xt,
> as independent the dummy and check box of suppress constant, I 
> then get:
> 
> Wald chi2(2)       =        13.24
> Log likelihood =  43.61512         Prob > chi2        =         
0.0013
> 
>           Coef.   Std. Err.      z        P>z     [95% Conf. 
> Interval]
> dumcl
> D1.   -.0707794    .060909    -1.16          0.245    -.1901589 
> .0486002 
> 
> ma
> L1.   -.6275359   .2040303    -3.08          0.002    -1.027428 -
> .227644/sigma    .0533161   .0078506     6.79    0.000        
> .0379293  .068703
> 
> Can I then conclude that there is evidence that the change in time
> period 22 is significant? Because coeff of D1 falls between the
> confidence interval?
> 
> What if I need to look at two events but that I do not know the exact
> date of the first intervention? (if look at graph of series on 
> verticaland time on horizontal seems that since timeperiod 13 or 
> 14 also change)
> Hope someone can help me.  Thank you!
> 
> 
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