Example 10.3: Kalman Filtering: Estimating an SSM Using the EM Algorithm

Time Series Analysis and Control Examples

Example 10.3: Kalman Filtering: Estimating an SSM Using the EM Algorithm

This example estimates the normal SSM of the mink-muskrat data 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:

$y_t & = & H{z}_t + \epsilon_t \z_t & = & F{z}_{t-1} + \eta_t$

where H is a 2 ×2 identity matrix, the observation noise has a time invariant covariance matrix R, and the covariance matrix of the transition equation is also assumed to be time invariant. The initial state z₀ has mean $\mu$ and covariance $\Sigma$ . For estimation, the $\Sigma$ matrix is fixed as

$[ 0.1 & 0.0 \ 0.0 & 0.1 ]$

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

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 using the M-step of the EM maximization

$F^{i+1} & = & S_t(1)[S_{t-1}(0)]^{-1} \V^{i+1} & = & \frac{1}T ( S_t(0) - S_t(... ...}_{t| T}) (y_t - H{z}_{t| T})^' + H{P}_{t| T} H^' ] \\mu^{i+1} & = & z_{0| T}$

where the index i represents the current iteration number, and

$S_t(0) & = & \sum_{t=1}^T (P_{t| T} + z_{t| T} z^'_{t| T}), \ S_t(1) & = & \sum_{t=1}^T (P_{t,t-1| T} + z_{t| T} z^'_{t-1| T})$

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

$P_{t-1,t-2| T} = P_{t-1| t-1} P^{*'_{t-2} + P^*_{t-1} (P_{t,t-1| T} - F{P}_{t-1| t-1}) P^{*'_{t-2}$

where $P^*_t = P_{t| t}F^'P^-_{t+1| t}$ and the initial value $P_{T,T-1| T}$ is derived using the formula

$P_{T,T-1| T} = [ I- P_{t| t-1} H^' (H{P}_{t| t-1} H^' + R) H ] F{P}_{T-1| T-1}$

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

$z_{1|} & = & F\mu^i \P_{1|} & = & F^i\Sigma F^{i' + V^i$

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

$\ell = -\frac{1}2 \sum_{t=1}^T \log(|{C}_t|) - \frac{1}2 \sum_{t=1}^T (y_t - H{z}_{t| t-1}) C^{-1}_t (y_t - H{z}_{t| t-1})^'$

where $C_t = H{P}_{t| t-1}H^' + R$ .

The iteration history is shown in Output 10.3.1. As shown in Output 10.3.2, the eigenvalues of F 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, p. 469). Finally, multistep forecasts of y_t are computed using the KALCVF call.

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

The predicted values of the state vector z_t and their standard errors are shown in Output 10.3.3.

   title 'SSM Estimation using EM Algorithm';
   data one;
      input y1 y2 @@;
   datalines;
      /*. . . data lines omitted . . .*/
   ;

   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;

   start main;
      use one;
      read all into y var {y1 y2};
     /*-- 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;
      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;
         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((logl-logl0)/(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 = teigval(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];
   finish;
   run;

Output 10.3.1: Iteration History

SSM Estimation using EM Algorithm

Iter	*-2log 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 10.3.2: Eigenvalues of F Matrix

Real	Imag	MOD
0.6547534	0.438317	0.7879237
0.6547534	-0.438317	0.7879237

Output 10.3.3: 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

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