Actual implementation of such formulae is always a little tedious because of various checks for missing values and other issues.  Anyway, here is an illustration.  The data step after the UCM call illustrates the RW R Square computation.  The formula in the doc essentially says that RW R Square = 1 - MSE/RWMSE where MSE is the mean error sum of squares of the residuals and RWMSE is the mean error sum of squares based on the first differences.  The two quantities could be based on different number of summands based on the missing value patterns.  Moreover, the first differences before the first time the non-missing residuals begin are ignored during the RWMSE computation.  Note that this is only a rough illustration.  The actual internal code might have more checks and other niceties. /*------------------------------------------------*/ proc ucm data=sashelp.air;    id date interval=month;    model air;    irregular;    level;    slope;    estimate;    forecast outfor=for lead=0; run; data fit;    set for(keep=date air residual);    retain sse 0; /* residual sum of squares */    retain rwss rwsum 0; /* for RWSSE computation */    retain nobs1 nobs2 0; /* count of summands */    retain firstNonMiss 0; /* First non-missing residual encountered */    dair = dif(air);    if residual ^= . then do;       sse = sse + residual*residual;    nobs1 = nobs1 + 1;    firstNonMiss = 1; /* remains 1 */  end;    if dair ^= . & firstNonMiss = 1 then do;    rwsum = rwsum + dair;       rwss = rwss + dair*dair;    nobs2 = nobs2 + 1;    end;    if date = '01dec1960'd then do;       /*rwsse based on sum[(x - mean(x))^2] = sum(x^2) - sum(x)*sum(x)/n */       rwsse = rwss - rwsum*rwsum/nobs2;    MRWSse = rwsse/nobs2;    MSE = sse/nobs1;       RWRSq = 1 - MSE/MRWSse;       output;    end; run; proc print data=fit;   var nobs1 nobs2 MSE MRWSse rwrsq; run; ... View more

I am glad you liked UCM. For even more flexibility, both in terms of types of time series data and the variety of state space models, you can check out the SSM procedure (also in SAS/ETS). The Kalman routines from IML are rarely needed if you have access to the UCM and SSM procedures. ... View more

I am glad you liked UCM. For even more flexibility, both in terms of types of time series data and the variety of state space models, you can check out the SSM procedure (also in SAS/ETS). The Kalman routines from IML are rarely needed if you have access to the UCM and SSM procedures. ... View more

I am glad things worked out. Rajesh ... View more

I am not particularly familiar with models where contemporaneous response values appear on the RHS.  In any event, I am going to suggest an alternate model that might capture many aspects of your problem description while still remaining parsimonious. Suppose (i,j)th buyer/seller pair denotes a panel. Model: B_it = mu_bi + mu_t[1] + X_t beta_1 + e_it S_jt = mu_sj + mu_t[2] + W_t beta_2 + e_jt Explanation: 1.  B_it and S_jt denote the i-th buyer and the j-th seller response values at time t. 2.  mu_bi and mu_sj denote the intercept terms for i-th buyer and j-th seller (fixed effects), 3. mu_t is a bivariate time trend such as random walk (taken to be a random effect term) that is common to all the panels, 4. X_t and W_t are regression variables (there might be overlap but their coefficients will be different for B and S), 5. e_it and e_jt are independent white noise terms. This model could be thought of as a bivariate version of panel model that has fixed panel effects and random time effects.   I am providing sample code for fitting this model with the SSM procedure. See the SSM procedure in SAS/ETS documentation for more information about this procedure.   For simplicity of descripton suppose that:    there are 100 buyers and 50 sellers,    X variables are X1 to X5    W variables are W1 to W8 Suppose the input data set, say test, has the following: 1.  the observations are indexed by a time index (say date) and test is sorted     by the time index. 2.  the time index are equispaced (such as monthly, daily, etc). 3.  there can be multiple observations, say n_t, at a time index t. n_t need not be     the same for all t (i.e., panels can be unbalanced) 4.  the input data set already has the necessary intercept dummies:     mu_b1 to mu_b100 are the buyer dummies: mu_bi = (buyer = i);     mu_s1 to mu_b50 are the seller dummies: mu_sj = (seller = j);     proc ssm data=test;    id date < interval=day >;    state timeEffect(2) t(I) cov(g) cov1(d);    comp bTime = timeEffect[1];    comp sTime = timeEffect[2];       irregular wb;    irregular ws;       model B = mu_b1-mu_b100 x1-x5 bTime wb;    model S = mu_s1-mu_s50 w1-w8 sTime ws;    output out=for press; run;     One can consider many other variations of this model, including your model with lagged response values.  However, PROC SSM is not very scalable for  such models when there are so many (thousands according to you) panels.  ... View more