This interesting project examines the applicability of the supercomputer in solving large-scale economic models. These models occur in macroeconomic analysis and in research on the financial sector of the economy. They are typically very complicated and nonlinear. They cannot be solved analytically, and so require simulations to be useful. The advent of relatively inexpensive and powerful computers, such as the vector processor, has greatly reduced the cost of simulation in many fields including economics. In statistics and econometrics, simulations usually take one of two forms, namely Monte Carlo studies, and parameter estimation via a technique known as bootstrapping. In the former, random samples are repeatedly generated from a known model, thus the statistical properties of the estimates of the model parameters can be ascertained. This is in essence a controlled experiment. In the latter approach, simulation is used to evaluate the statistical properties of parameter estimates obtained from a particular sample by repeatedly randomly generating data that are similar to the original data and examining the statistical properties of the resulting parameter estimates. Each procedure has advantages and disadvantages. This project examines these two analytical techinques in detail using the supercomputer. In particular they are applied to large economic factor analysis models of the financial sector to test various theories of stock price movements and asset pricing and accumulation.