% PURPOSE: demonstrate the use of recm%          function to estimate a Bayesian%          error correction model using Random-walk%          averaging prior% References: LeSage and Krivelyova (1998) % ``A Spatial Prior for Bayesian Vector Autoregressive Models'',% forthcoming Journal of Regional Science, (on http://www.econ.utoledo.edu)% and% LeSage and Krivelova (1997) (on http://www.econ.utoledo.edu)% ``A Random Walk Averaging Prior for Bayesian Vector Autoregressive Models''             % ---------------------------------------------% usage: recm_d% ----------------------------------------------load test.dat; % a test data set containing               % monthly mining employment for               % il,in,ky,mi,oh,pa,tn,wv% data covers 1982,1 to 1996,5vnames =  ['  il',           '  in',               '  ky',               '  mi',               '  oh',               '  pa',               '  tn',               '  wv'];         y = test;[nobs neqs] = size(y);nlag = 9;  % number of lags in var-model% prior hyperparameters% priors for contiguous variables:  N(w(i,j),sig) for 1st own lag%                                  N(  0 ,tau*sig/k) for lag k=2,...,nlag%               % priors for non-contiguous variables are:  N(w(i,j) ,theta*sig/k) for lag k %  % e.g., if y1, y3, y4 are contiguous variables in eq#1, y2 non-contiguous%  w(1,1) = 1/3, w(1,3) = 1/3, w(1,4) = 1/3, w(1,2) = 0%                                              % typical values would be: sig = .1-.3, tau = 4-8, theta = .5-1  sig = 0.1;tau = 6;theta = 0.5;freq = 12;   % monthly data% this is an example of using 1st-order contiguity% of the states as weights to produce prior meansW=[0      0.5    0.5    0     0     0    0     0   0.25   0      0.25   0.25  0.25  0    0     0   0.20   0.20   0      0     0.20  0    0.20  0.20   0      0.50   0      0     0.50  0    0     0   0      0.20   0.20   0.20  0     0.20 0.20  0.20   0      0      0      0     0.50  0    0     0.50   0      0      1      0     0     0    0     0   0      0      0.33   0     0.33  0.33 0     0];% estimate the model% let routine determine # of co-integrating vectorsresult = recm(y,nlag,W,freq,sig,tau,theta);% print results to a filefid = fopen('recm.out','w');prt_var(result,vnames,fid);fclose(fid);