function [w, A, C, sbc, fpe, th]=arfit(v, pmin, pmax, selector, no_const)%ARFIT	Stepwise least squares estimation of multivariate AR model.%%  [w,A,C,SBC,FPE,th]=ARFIT(v,pmin,pmax) produces estimates of the%  parameters of a multivariate AR model of order p,%%      v(k,:)' = w' + A1*v(k-1,:)' +...+ Ap*v(k-p,:)' + noise(C),%%  where p lies between pmin and pmax and is chosen as the optimizer%  of Schwarz's Bayesian Criterion. The input matrix v must contain%  the time series data, with columns of v representing variables%  and rows of v representing observations.  ARFIT returns least%  squares estimates of the intercept vector w, of the coefficient%  matrices A1,...,Ap (as A=[A1 ... Ap]), and of the noise covariance%  matrix C.%%  As order selection criteria, ARFIT computes approximations to%  Schwarz's Bayesian Criterion and to the logarithm of Akaike's Final%  Prediction Error. The order selection criteria for models of order%  pmin:pmax are returned as the vectors SBC and FPE.%%  The matrix th contains information needed for the computation of%  confidence intervals. ARMODE and ARCONF require th as input%  arguments.%       %  If the optional argument SELECTOR is included in the function call,%  as in ARFIT(v,pmin,pmax,SELECTOR), SELECTOR is used as the order%  selection criterion in determining the optimum model order. The%  three letter string SELECTOR must have one of the two values 'sbc'%  or 'fpe'. (By default, Schwarz's criterion SBC is used.) If the%  bounds pmin and pmax coincide, the order of the estimated model%  is p=pmin=pmax. %%  If the function call contains the optional argument 'zero' as the%  fourth or fifth argument, a model of the form%%         v(k,:)' = A1*v(k-1,:)' +...+ Ap*v(k-p,:)' + noise(C) %%  is fitted to the time series data. That is, the intercept vector w%  is taken to be zero, which amounts to assuming that the AR(p)%  process has zero mean.%  Modified 14-Oct-00%  Authors: Tapio Schneider%           tapio@gps.caltech.edu%%           Arnold Neumaier%           neum@cma.univie.ac.at  % n: number of observations; m: dimension of state vectors  [n,m]   = size(v);       if (pmin ~= round(pmin) | pmax ~= round(pmax))    error('Order must be integer.');  end  if (pmax < pmin)    error('PMAX must be greater than or equal to PMIN.')  end  % set defaults and check for optional arguments  if (nargin == 3)              % no optional arguments => set default values    mcor       = 1;               % fit intercept vector    selector   = 'sbc';	          % use SBC as order selection criterion  elseif (nargin == 4)          % one optional argument    if strcmp(selector, 'zero')      mcor     = 0;               % no intercept vector to be fitted      selector = 'sbc';	          % default order selection     else      mcor     = 1; 		  % fit intercept vector    end  elseif (nargin == 5)          % two optional arguments    if strcmp(no_const, 'zero')      mcor     = 0;               % no intercept vector to be fitted    else      error(['Bad argument. Usage: ', ...	     '[w,A,C,SBC,FPE,th]=AR(v,pmin,pmax,SELECTOR,''zero'')'])    end  end  ne  	= n-pmax;               % number of block equations of size m  npmax	= m*pmax+mcor;          % maximum number of parameter vectors of length m  if (ne <= npmax)    error('Time series too short.')  end  % compute QR factorization for model of order pmax  [R, scale]   = arqr(v, pmax, mcor);  % compute approximate order selection criteria for models   % of order pmin:pmax  [sbc, fpe]   = arord(R, m, mcor, ne, pmin, pmax);  % get index iopt of order that minimizes the order selection   % criterion specified by the variable selector  [val, iopt]  = min(eval(selector));   % select order of model  popt         = pmin + iopt-1; % estimated optimum order   np           = m*popt + mcor; % number of parameter vectors of length m  % decompose R for the optimal model order popt according to   %  %   | R11  R12 |  % R=|          |  %   | 0    R22 |  %  R11   = R(1:np, 1:np);  R12   = R(1:np, npmax+1:npmax+m);      R22   = R(np+1:npmax+m, npmax+1:npmax+m);  % get augmented parameter matrix Aaug=[w A] if mcor=1 and Aaug=A if mcor=0  if (np > 0)       if (mcor == 1)      % improve condition of R11 by re-scaling first column      con 	= max(scale(2:npmax+m)) / scale(1);       R11(:,1)	= R11(:,1)*con;     end;    Aaug = (R11\R12)';        %  return coefficient matrix A and intercept vector w separately    if (mcor == 1)      % intercept vector w is first column of Aaug, rest of Aaug is       % coefficient matrix A      w = Aaug(:,1)*con;        % undo condition-improving scaling      A = Aaug(:,2:np);    else      % return an intercept vector of zeros       w = zeros(m,1);      A = Aaug;    end  else    % no parameters have been estimated     % => return only covariance matrix estimate and order selection     % criteria for ``zeroth order model''      w   = zeros(m,1);    A   = [];  end    % return covariance matrix  dof   = ne-np;                % number of block degrees of freedom  C     = R22'*R22./dof;        % bias-corrected estimate of covariance matrix    % for later computation of confidence intervals return in th:   % (i)  the inverse of U=R11'*R11, which appears in the asymptotic   %      covariance matrix of the least squares estimator  % (ii) the number of degrees of freedom of the residual covariance matrix   invR11 = inv(R11);  if (mcor == 1)    % undo condition improving scaling    invR11(1, :) = invR11(1, :) * con;  end  Uinv   = invR11*invR11';  th     = [dof zeros(1,size(Uinv,2)-1); Uinv];