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st: Time series: vecm and recursive window forecasts


From   "Svein.Oskar Lauvsnes" <[email protected]>
To   <[email protected]>
Subject   st: Time series: vecm and recursive window forecasts
Date   Fri, 08 Dec 2006 14:47:03 +0100

Hi,
I am investigating the predictive abilities of macrovariables on stock market returns. So far I have made 1-step ahead predictions from single equation models, keeping the starting point fixed and for each new regression extending the dataset by one observation. I would like to compare the single equation forecasts with forecasts from a system of equations such as a vector error correction model (and perhaps a VAR in first differences). I have used the following program for my single equation forecasts:
 
 gen time = _n
  tsset time
  
   
    
  capture program drop rforecast
  program rforecast, rclass
     syntax [if]
     regress dose l.dose dnib `if'   
     summ time if e(sample)     
     local last = r(max)
     local fcast = _b[_cons] + _b[L.dose]*dose[`last']///
    + _b[dnib]*nib[`last'+1] 
 
   return scalar forecast = `fcast'
   return scalar actual = dose[`last' +1]
  end
 
  rolling actual=r(actual) forecast=r(forecast), recursive ///
  window(149) saving(myrolling, replace): rforecast
 
  use myrolling, clear
  list in 1/100

Hopefully, the program will work on a VECM by substituting the sentences in bold? How should I modify my program to do rolling window estimation/forecasting using a VECM? I suppose the number of cointegrating vectors and lags would have to be fixed (if n variables in the system, there may be n-1 cointegrating relations) . I hope you can help me with this one.
 
Sincerely
 
Svein Lauvsnes
Bodoe Graduate School of Business, Norway
     



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