Time Series Analysis and Examples

The following example estimates the normal SSM of the mink-muskrat data (Harvey, 1989) by using the EM algorithm. The mink-muskrat data are log-counts that have been detrended.

title 'SSM Estimation Using EM Algorithm';
data MinkMuskrat;
   input muskrat mink @@;
datalines;
 0.10609  0.16794 -0.16852  0.06242 -0.23700 -0.13344
-0.18022 -0.50616  0.18094 -0.37943  0.65983 -0.40132
 0.65235  0.08789  0.21594  0.23877 -0.11515  0.40043
-0.00067  0.37758 -0.00387  0.55735 -0.25202  0.34444
-0.65011 -0.02749 -0.53646 -0.41519 -0.08462  0.02591
-0.05640 -0.11348  0.26630  0.20544  0.03641  0.16331
-0.26030 -0.01498 -0.03995  0.09657  0.33612  0.31096
-0.11672  0.30681 -0.69775 -0.69351 -0.07569 -0.56212
 0.36149 -0.36799  0.42341 -0.24725  0.26721  0.04478
-0.00363  0.21637  0.08333  0.30188 -0.22480  0.29493
-0.13728  0.35463 -0.12698  0.05490 -0.18770 -0.52573
 0.34741 -0.49541  0.54947 -0.26250  0.57423 -0.21936
 0.57493 -0.12012  0.28188  0.63556 -0.58438  0.27067
-0.50236  0.10386 -0.60766  0.36748 -1.04784 -0.33493
-0.68857 -0.46525 -0.11450 -0.63648  0.22005 -0.26335
 0.36533  0.07017 -0.00151 -0.04977  0.03740 -0.02411
 0.22438  0.30790 -0.16196  0.41050 -0.12862  0.34929
 0.08448 -0.14995  0.17945 -0.03320  0.37502  0.02953
 0.95727  0.24090  0.86188  0.41096  0.39464  0.24157
 0.53794  0.29385  0.13054  0.39336 -0.39138 -0.00323
-1.23825 -0.56953 -0.66286 -0.72363 
;

Because this EM algorithm uses filtering and smoothing, you can use the KALCVF and KALCVS calls to analyze the data. Consider the bivariate SSM,

where is a identity matrix, the observation noise has a time-invariant covariance matrix , and the covariance matrix of the transition equation is also assumed to be time-invariant. The initial state has mean and covariance . For estimation, the matrix is fixed as

whereas the mean vector is updated by the smoothing procedure such that . Note that this estimation requires an extra smoothing step, because the usual smoothing procedure does not produce .

The EM algorithm maximizes the expected log-likelihood function, given the current parameter estimates. In practice, the log-likelihood function of the normal SSM is evaluated while the parameters are updated by using the M-step of the EM maximization,

where the index i represents the current iteration number, and

It is necessary to compute the value of recursively such that

where and the initial value is derived by using the formula

Note that the initial value of the state vector is updated for each iteration,

The objective function value is computed as in the SAS/IML module LIK. The log-likelihood function is written as

where .

The EM algorithm is implemented by the following statements. The iteration history is shown in Output 13.2.1.

proc iml;
start lik(y,pred,vpred,h,rt);
   n = nrow(y);
   nz = ncol(h);
   et = y - pred*h`;
   sum1 = 0;
   sum2 = 0;
   do i = 1 to n;
      vpred_i = vpred[(i-1)*nz+1:i*nz,];
      et_i = et[i,];
      ft = h*vpred_i*h` + rt;
      sum1 = sum1 + log(det(ft));
      sum2 = sum2 + et_i*inv(ft)*et_i`;
   end;
   return(sum1+sum2);
finish;

use MinkMuskrat;
read all into y var {muskrat mink};
close MinkMuskrat;

/*-- mean adjust series --*/
t = nrow(y);   ny = ncol(y);   nz = ny;
f = i(nz);
h = i(ny);

/*-- observation noise variance is diagonal --*/
rt = 1e-5#i(ny);

/*-- transition noise variance --*/
vt = .1#i(nz);
a = j(nz,1,0);
b = j(ny,1,0);
myu = j(nz,1,0);
sigma = .1#i(nz);
converge = 0;
logl0 = 0.0;
do iter = 1 to 100 while( converge = 0 );

/*--- construct big cov matrix --*/
var = ( vt || j(nz,ny,0) ) //
      ( j(ny,nz,0) || rt );

/*-- initial values are changed --*/
z0  = myu` * f`;
vz0 = f * sigma * f` + vt;

/*-- filtering to get one-step prediction and filtered value --*/
call kalcvf(pred,vpred,filt,vfilt,y,0,a,f,b,h,var,z0,vz0);

/*-- smoothing using one-step prediction values --*/
call kalcvs(sm,vsm,y,a,f,b,h,var,pred,vpred);

/*-- compute likelihood values --*/
logl = lik(y,pred,vpred,h,rt);

/*-- store old parameters and function values --*/
myu0 = myu;
f0 = f;
vt0 = vt;
rt0 = rt;
diflog = logl - logl0;
logl0 = logl;
itermat = itermat // ( iter || logl0 || shape(f0,1) || myu0` );

/*-- obtain P*(t) to get P_T_0 and Z_T_0   --*/
/*-- these values are not usually needed   --*/
/*-- See Harvey (1989, p154) or Shumway (1988, p177) --*/
jt1 = sigma * f` * inv(vpred[1:nz,]);
p_t_0  = sigma + jt1*(vsm[1:nz,] - vpred[1:nz,])*jt1`;
z_t_0  = myu + jt1*(sm[1,]` - pred[1,]`);
p_t1_t  = vpred[(t-1)*nz+1:t*nz,];
p_t1_t1 = vfilt[(t-2)*nz+1:(t-1)*nz,];
kt = p_t1_t*h`*inv(h*p_t1_t*h`+rt);

/*-- obtain P_T_TT1. See Shumway (1988, p180) --*/
p_t_ii1 = (i(nz)-kt*h)*f*p_t1_t1;
st0 = vsm[(t-1)*nz+1:t*nz,] + sm[t,]`*sm[t,];
st1 = p_t_ii1 + sm[t,]`*sm[t-1,];
st00 = p_t_0 + z_t_0 * z_t_0`;
cov = (y[t,]` - h*sm[t,]`) * (y[t,]` - h*sm[t,]`)` +
      h*vsm[(t-1)*nz+1:t*nz,]*h`;
do i = t to 2 by -1;
   p_i1_i1 = vfilt[(i-2)*nz+1:(i-1)*nz,];
   p_i1_i  = vpred[(i-1)*nz+1:i*nz,];
   jt1 = p_i1_i1 * f` * inv(p_i1_i);
   p_i1_i  = vpred[(i-2)*nz+1:(i-1)*nz,];
   if ( i > 2 ) then
      p_i2_i2 = vfilt[(i-3)*nz+1:(i-2)*nz,];
   else
      p_i2_i2 = sigma;
   jt2 = p_i2_i2 * f` * inv(p_i1_i);
   p_t_i1i2 = p_i1_i1*jt2` + jt1*(p_t_ii1 - f*p_i1_i1)*jt2`;
   p_t_ii1 = p_t_i1i2;
   temp = vsm[(i-2)*nz+1:(i-1)*nz,];
   sm1 = sm[i-1,]`;
   st0 = st0 + ( temp + sm1 * sm1` );
   if ( i > 2 ) then
      st1 = st1 + ( p_t_ii1 + sm1 * sm[i-2,]);
   else st1 = st1 + ( p_t_ii1 + sm1 * z_t_0`);
   st00 = st00 + ( temp + sm1 * sm1` );
   cov = cov + ( h * temp * h` +
         (y[i-1,]` - h * sm1)*(y[i-1,]` - h * sm1)` );
end;

/*-- M-step: update the parameters --*/
myu = z_t_0;
f = st1 * inv(st00);
vt = (st0 - st1 * inv(st00) * st1`)/t;
rt = cov / t;

/*-- check convergence --*/
if ( max(abs((myu - myu0)/(myu0+1e-6))) < 1e-2 &
     max(abs((f - f0)/(f0+1e-6))) < 1e-2 &
     max(abs((vt - vt0)/(vt0+1e-6))) < 1e-2 &
     max(abs((rt - rt0)/(rt0+1e-6))) < 1e-2 &
     abs((diflog)/(logl0+1e-6)) < 1e-3 ) then
   converge = 1;
end;

reset noname;
   colnm = {'Iter' '-2*log L' 'F11' 'F12' 'F21' 'F22'
         'MYU11' 'MYU22'};
print itermat[colname=colnm format=8.4];

The following statements compute the eigenvalues of . As shown in Output 13.2.2, the eigenvalues of are within the unit circle, indicating that the SSM is stationary. However, the muskrat series is reported to be difference stationary. The estimated parameters are almost identical to those of the VAR(1) estimates. See Harvey (1989).

eval = eigval(f0);
colnm = {'Real' 'Imag' 'MOD'};
eval = eval || sqrt((eval#eval)[,+]);
print eval[colname=colnm];

Finally, multistep forecasts of are computed by calling the KALCVF subroutine. The predicted values of the state vector and their standard errors are shown in Output 13.2.3.

var = ( vt || j(nz,ny,0) ) //
      ( j(ny,nz,0) || rt );

/*-- initial values are changed --*/
z0  = myu` * f`;
vz0 = f * sigma * f` + vt;
free itermat;

/*-- multistep prediction --*/
call kalcvf(pred,vpred,filt,vfilt,y,15,a,f,b,h,var,z0,vz0);
do i = 1 to 15;
   itermat = itermat // ( i || pred[t+i,] ||
             sqrt(vecdiag(vpred[(t+i-1)*nz+1:(t+i)*nz,]))` );
end;
colnm = {'n-Step' 'Z1_T_n' 'Z2_T_n' 'SE_Z1' 'SE_Z2'};
print itermat[colname=colnm];
quit;