Specialize mod file for linear Kalman filter.

The value of the likelihood at the mod will be used to test various
nonlinear filters.

tests/particle/noisy_ar1.mod

 var x y;
-varexo u v;
+varexo v;
 
-parameters c, r;
+parameters r;
 
-c = .500;
 r = .975;
 
 model;
-y = x + u;
-x = r*x(-1) + 0.0000001*x(1) + v;
+  y = x;
+  x = r*x(-1) + v;
 end;
 
 shocks;
-var u; stderr sqrt(0.02);
-var v; stderr sqrt(0.02);
+  var v; stderr sqrt(0.02);
 end;
 
 steady;
 
 check;
 
-stoch_simul(irf=0, periods=10000);
-
-datatomfile('noisy_ar1_data');
+stoch_simul(irf=0,periods=0,order=2);
 
 estimated_params;
-c,  .500;
-r,  .975;
-stderr u, sqrt(.2);
-stderr v, sqrt(.02);
+  r,  .975, uniform_pdf,,,0,1;
+  stderr y, sqrt(.2), uniform_pdf,,,0,1;
+  stderr v, sqrt(.02), uniform_pdf,,,0,1;
 end;
 
 varobs y;
 
 
-particle = 1;
-
-if particle;
-options_.particle.status = 1;
-options_.particle.initialization = 1;
-options_.particle.pruning = false;
-options_.particle.number_of_particles = 20000;
-options_.particle.resampling.status = 'systematic';
-options_.particle.resampling.neff_threshold = .1;
-
-options_.gstep(1) = 1e-4;
-options_.gstep(2) = .1;
-
-options_.mode_check_neighbourhood_size = 0.05;
+estimation(nobs=100,datafile=noisy_ar1_data,mh_replic=0, mode_compute=4, mode_check);
 
-set_dynare_threads('local_state_space_iteration_2',3);
+copyfile('noisy_ar1/Output/noisy_ar1_mode.mat', 'noisy.mat')
 
-estimation(order=2,nobs=100,datafile=linear_model_data,mh_replic=0,filter_algorithm=sis);
-
-else;
-
-estimation(nobs=100,datafile=noisy_ar1_data,mh_replic=0);
-
-end;
+ovalue = oo_.posterior.optimization.log_density;
+save('optimal_value_with_linear_kalman_filter', 'ovalue')