Kalman Filtering
Previous Page|Next Page

Example 13.3 Kalman Filtering: SSM Estimation With the EM Algorithm

The following example estimates the normal SSM of the mink-muskrat data by using the EM algorithm. The mink-muskrat series are detrended. Refer to Harvey (1989) for details of this data set. Since this EM algorithm uses filtering and smoothing, you can learn how to use the KALCVF and KALCVS calls to analyze the data. Consider the bivariate SSM:

\begin{eqnarray*} \textbf{y}_ t & = & \bH \textbf{z}_ t + \epsilon _ t \\ \textbf{z}_ t & = & \bF \textbf{z}_{t-1} + \eta _ t \end{eqnarray*}

where $\bH $ is a $2 \times 2$ identity matrix, the observation noise has a time-invariant covariance matrix $\bR $, and the covariance matrix of the transition equation is also assumed to be time invariant. The initial state $\textbf{z}_0$ has mean $\mu $ and covariance $\Sigma $. For estimation, the $\Sigma $ matrix is fixed as

\[ \left[ \begin{array}{cc} 0.1 & 0.0 \\ 0.0 & 0.1 \end{array} \right] \]

while the mean vector $\mu $ is updated by the smoothing procedure such that $\hat{\mu } = \textbf{z}_{0|T}$. Note that this estimation requires an extra smoothing step since the usual smoothing procedure does not produce $\textbf{z}_{T|0}$.

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

\begin{eqnarray*} \bF ^{i+1} & = & \bS _ t(1)[\bS _{t-1}(0)]^{-1} \\ \bV ^{i+1} & = & \frac{1}{T} \left( \bS _ t(0) - \bS _ t(1)[\bS _{t-1}(0)]^{-1} \bS ^{\prime }_ t(1) \right) \\ \bR ^{i+1} & = & \frac{1}{T} \sum _{t=1}^ T \left[ (\textbf{y}_ t - \bH \textbf{z}_{t|T}) (\textbf{y}_ t - \bH \textbf{z}_{t|T})^{\prime } + \bH \bP _{t|T} \bH ^{\prime } \right] \\ \mu ^{i+1} & = & \textbf{z}_{0|T} \end{eqnarray*}

where the index $i$ represents the current iteration number, and

\begin{eqnarray*} \bS _ t(0) & = & \sum _{t=1}^ T (\bP _{t|T} + \textbf{z}_{t|T} \textbf{z}^{\prime }_{t|T}), \\ \bS _ t(1) & = & \sum _{t=1}^ T (\bP _{t,t-1|T} + \textbf{z}_{t|T} \textbf{z}^{\prime }_{t-1|T}) \end{eqnarray*}

It is necessary to compute the value of $\bP _{t,t-1|T}$ recursively such that

\[ \bP _{t-1,t-2|T} = \bP _{t-1|t-1} \bP ^{*\prime }_{t-2} + \bP ^*_{t-1} (\bP _{t,t-1|T} - \bF \bP _{t-1|t-1}) \bP ^{*\prime }_{t-2} \]

where $\bP ^*_ t = \bP _{t|t}\bF ^{\prime }\bP ^-_{t+1|t}$ and the initial value $\bP _{T,T-1|T}$ is derived by using the formula

\[ \bP _{T,T-1|T} = \left[ \bI - \bP _{t|t-1} \bH ^{\prime } (\bH \bP _{t|t-1} \bH ^{\prime } + \bR )^{-1} \bH \right] \bF \bP _{T-1|T-1} \]

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

\begin{eqnarray*} \textbf{z}_{1|0} & = & \bF \mu ^ i \\ \bP _{1|0} & = & \bF ^ i\Sigma \bF ^{i\prime } + \bV ^ i \end{eqnarray*}

The objective function value is computed as $-2\ell $ in the IML module LIK. The log-likelihood function is written

\[ \ell = -\frac{1}{2} \sum _{t=1}^ T \log (|\bC _ t|) - \frac{1}{2} \sum _{t=1}^ T (\textbf{y}_ t - \bH \textbf{z}_{t|t-1}) \bC ^{-1}_ t (\textbf{y}_ t - \bH \textbf{z}_{t|t-1})^{\prime } \]

where $\bC _ t = \bH \bP _{t|t-1}\bH ^{\prime } + \bR $.

The iteration history is shown in Output 13.3.1. As shown in Output 13.3.2, the eigenvalues of $\bF $ are within the unit circle, which indicates that the SSM is stationary. However, the muskrat series (Y1) is reported to be difference stationary. The estimated parameters are almost identical to those of the VAR(1) estimates. Refer to Harvey (1989). Finally, multistep forecasts of $\textbf{y}_ t$ are computed by using the KALCVF call. Here is the code: 

   call kalcvf(pred,vpred,filt,vfilt,y,15,a,f,b,h,var,z0,vz0);

The predicted values of the state vector $\textbf{z}_ t$ and their standard errors are shown in Output 13.3.3. Here is the code: 

 title 'SSM Estimation using EM Algorithm';
 data one;
    input y1 y2 @@;
 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 
 ;
 run;

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 one;
read all into y var {y1 y2};
close one;
/*-- 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];

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

Output 13.3.1: Iteration History

SSM Estimation using EM Algorithm

Iter -2*log L F11 F12 F21 F22 MYU11 MYU22
1.0000 -154.010 1.0000 0.0000 0.0000 1.0000 0.0000 0.0000
2.0000 -237.962 0.7952 -0.6473 0.3263 0.5143 0.0530 0.0840
3.0000 -238.083 0.7967 -0.6514 0.3259 0.5142 0.1372 0.0977
4.0000 -238.126 0.7966 -0.6517 0.3259 0.5139 0.1853 0.1159
5.0000 -238.143 0.7964 -0.6519 0.3257 0.5138 0.2143 0.1304
6.0000 -238.151 0.7963 -0.6520 0.3255 0.5136 0.2324 0.1405
7.0000 -238.153 0.7962 -0.6520 0.3254 0.5135 0.2438 0.1473
8.0000 -238.155 0.7962 -0.6521 0.3253 0.5135 0.2511 0.1518
9.0000 -238.155 0.7962 -0.6521 0.3253 0.5134 0.2558 0.1546
10.0000 -238.155 0.7961 -0.6521 0.3253 0.5134 0.2588 0.1565


Output 13.3.2: Eigenvalues of F Matrix

Real Imag MOD
0.6547534 0.438317 0.7879237
0.6547534 -0.438317 0.7879237


Output 13.3.3: Multistep Prediction

n-Step Z1_T_n Z2_T_n SE_Z1 SE_Z2
1 -0.055792 -0.587049 0.2437666 0.237074
2 0.3384325 -0.319505 0.3140478 0.290662
3 0.4778022 -0.053949 0.3669731 0.3104052
4 0.4155731 0.1276996 0.4021048 0.3218256
5 0.2475671 0.2007098 0.419699 0.3319293
6 0.0661993 0.1835492 0.4268943 0.3396153
7 -0.067001 0.1157541 0.430752 0.3438409
8 -0.128831 0.0376316 0.4341532 0.3456312
9 -0.127107 -0.022581 0.4369411 0.3465325
10 -0.086466 -0.052931 0.4385978 0.3473038
11 -0.034319 -0.055293 0.4393282 0.3479612
12 0.0087379 -0.039546 0.4396666 0.3483717
13 0.0327466 -0.017459 0.439936 0.3485586
14 0.0374564 0.0016876 0.4401753 0.3486415
15 0.0287193 0.0130482 0.440335 0.3487034


Previous Page|Next Page|Top of Page
Copyright © SAS Institute Inc. All Rights Reserved.