Saving Time with Large Data Sets

If your input data set has many observations, the FIT statement performs a large number of model program executions. A pass through the data is made at least once for each iteration and the model program is executed once for each observation in each pass. If you refine the starting estimates by using a smaller data set, the final estimation with the full data set might require fewer iterations.

For example, you could use

   proc model;
      /* Model goes here */
      fit / data=a(obs=25);
      fit / data=a;

where OBS=25 selects the first 25 observations in A. The second FIT statement produces the final estimates using the full data set and starting values from the first run.

Fitting the Model in Sections to Save Space and Time

If you have a very large model (with several hundred parameters, for example), the procedure uses considerable space and time. You might be able to save resources by breaking the estimation process into several steps and estimating the parameters in subsets.

You can use the FIT statement to select for estimation only the parameters for selected equations. Do not break the estimation into too many small steps; the total computer time required is minimized by compromising between the number of FIT statements that are executed and the size of the crossproducts matrices that must be processed.

When the parameters are estimated for selected equations, the entire model program must be executed even though only a part of the model program might be needed to compute the residuals for the equations selected for estimation. If the model itself can be broken into sections for estimation (and later combined for simulation and forecasting), then more resources can be saved.

For example, to estimate the following four equation model in two steps, you could use

   proc model data=a outmodel=part1;
      parms a0-a2 b0-b2 c0-c3 d0-d3;
      y1 = a0 + a1*y2 + a2*x1;
      y2 = b0 + b1*y1 + b2*x2;
      y3 = c0 + c1*y1 + c2*y4 + c3*x3;
      y4 = d0 + d1*y1 + d2*y3 + d3*x4;
      fit y1 y2;
      fit y3 y4;
      fit y1 y2 y3 y4;
   run;

You should try estimating the model in pieces to save time only if there are more than 14 parameters; the preceding example takes more time, not less, and the difference in memory required is trivial.

Memory Requirements for Parameter Estimation

PROC MODEL is a large program, and it requires much memory. Memory is also required for the SAS System, various data areas, the model program and associated tables and data vectors, and a few crossproducts matrices. For most models, the memory required for PROC MODEL itself is much larger than that required for the model program, and the memory required for the model program is larger than that required for the crossproducts matrices.

The number of bytes needed for two crossproducts matrices, four S matrices, and three parameter covariance matrices is

plus lower-order terms, where m is the number of unique nonzero derivatives of each residual with respect to each parameter, g is the number of equations, k is the number of instruments, and p is the number of parameters. This formula is for the memory required for 3SLS. If you are using OLS, a reasonable estimate of the memory required for large problems (greater than 100 parameters) is to divide the value obtained from the formula in half.

Consider the following model program.

 
   proc model data=test2 details;
      exogenous x1 x2;
      parms b1 100 a1 a2 b2 2.5 c2 55;
      y1 = a1 * y2 + b1 * x1 * x1;
      y2 = a2 * y1 + b2 * x2 * x2 + c2 / x2;
      fit y1 y2 / n3sls memoryuse;
      inst b1 b2 c2 x1 ;
   run;

The DETAILS option prints the storage requirements information shown in Figure 18.36.