Kalman filter estimation
[rkalman]=kalman(func,y,x,z,F,param,varargin)
func = the function which transforms the parameters into the matrix of variances (Q and R)
y = (nobs x 1) dependent variable vector
x = (nobs x 1) explanatory variable matrix
z = (nxl) data matrix of exogenous variables (or [] if there are no exogenous variables in the model) .TP
F = the transfer matrix
param = a vector of parameters (sqrt of variances)
varargin = optional arguments which can be:
- 'priorb0=x' where x is (k x 1) vector with prior b0 values (default = zeros(k,1), diffuse)
- 'priorv0=x' where x = (k x k) matrix with prior variance for Q (default = eye(k)*1e+5, a diffuse prior)
- 'meth=x' where x is either 'maxlik' (default) or 'optim' according to the optimization program used
- any option to maxlik (see maxlik() for a list)
rkalman = a results tlist with
- rkalman('meth') = 'kalman'
- rkalman('Q') = estimated Q
- rkalman('R') = estimated R
- rkalman('priorb0') = B(0/0)
- rkalman('priorv0') = sigma(0/0)
- rkalman('betat') = B(t/t)
- rkalman('betaf') = B(t/t-1)
- rkalman('betas') = B(t/T)
- rkalman('sigmatt') = sigma(t/t)
- rkalman('sigmatf') = sigma(t/t-1)
- rkalman('sigmats') = sigma(t/T)
- rkalman('param') = estimated parameters
- rkalman('vcov') = variance-covariance matrix of estimated parameters
- rkalman('tstat') = Student's t of estimated parameters
- rkalman('y') = y
- rkalman('x') = x
- rkalman('yhat') = X(t)*B(t)
- rkalman('resid') = y-X*B(t)
- rkalman('like') = log-likelihood
- rkalman('nobs') = # of observations
- rkalman('nvar') = # of exogenous variables