Recent empirical analyses using the Euler Equation approach have repeatedly rejected the stochastic implications of the Rational Expectations-Life Cycle Hypothesis. Several authors have argued that empirical results using aggregated data are inconclusive because of aggregation bias. Statistical conclusions from panel studies using food expenditure proxies are suspect as well. This study reexamines the stochastic implications of the hypothesis using a broader expenditure definition constructed from the Consumer Expenditure Survey. The study constructs sample orthogonality conditions from the underlying nonlinear Euler equations, and thereby relaxes the linear assumptions applied in previous studies. If the error structure of the model is conditionally heteroscedastic, it is shown that linearizing can produce bias and inconsistent estimation. For this study, hypothesis tests and parameter estimates are attained using the numerical solution of the generalized method of moments minimization problem. The hypothesis-testing methodology is flexible in that it tests the "pure" Rational Expectations Hypothesis against a borrowing constraint hypothesis through a split sample technique. Various test results from this study consistently show that borrowing constraints do affect consumption growth of financially strapped households. These conclusions are not as sharp when using food expenditure data alone, however.