*
* Example of bootstrapping an FMOLS estimate of cointegrating vectors.
* Based upon the technique described in Li and Maddala, "Bootstrapping
* cointegrating regressions", J. of Econometrics 80 (1997) pp 297-318
*
open data kpswdata.rat
calendar(q) 1947
all 1988:04
data(format=rats) / c in y mp dp r
*
@fm(lags=5,det=constant) 1954:1 *
# mp y r
*
* Get the FM residuals and the estimated coefficient vector. Depending
* upon how you're planning to use the bootstrap, you might want to make
* b equal to something else (like the value the cointegrating vector
* would take under a null hypothesis).
*
set u = %resids
compute b=%beta
*
* Difference the RHS endogenous variables
*
diff y / dy
diff r / dr
compute [vector] betamean=%zeros(3,1)
compute [symm]   betacmom=%zeros(3,3)
*
compute ndraws=1000
do draws=1,ndraws
   *
   * Do a block bootstrap for the stationary elements.
   *
   boot(block=10,method=stationary) entries 1954:1 *
   *
   * The first differences and the residuals are resampled together.
   * Rebuild the RHS endogenous variables, and then the dependent
   * variable
   *
   set(first=0.0) resampy 1953:4 1988:4 = resampy{1}+dy(entries(t))
   set(first=0.0) resampr 1953:4 1988:4 = resampr{1}+dr(entries(t))
   set resampmp 1954:1 1988:4 = b(1)+b(2)*resampy+b(3)*resampr+u(entries(t))
   *
   * Re-estimate and update the betamean and betacmom matrices
   *
   @fm(lags=5,det=constant,noprint) 1954:1 *
   # resampmp resampy resampr
   compute betamean=betamean+%beta-b
   compute betacmom=betacmom+%outerxx(%beta-b)
end do draws
compute betamean=betamean/ndraws
compute betacmom=betacmom/ndraws
linreg(create,coeffs=b,covmat=betacmom,noscale) mp
# constant y r