This paper considers tests for structural instability of short duration, such as at the end of the sample. The key feature of the testing problem is that the number, m , of observations in the period of potential change is relatively small — possibly as small as one. The well-known F test of Chow (1960) for this problem only applies in a linear regression model with normally distributed iid errors and strictly exogenous regressors, even when the total number of observations, n + m , is large. We generalize the F test to cover regression models with much more general error processes, regressors that are not strictly exogenous, and estimation by instrumental variables as well as least squares. In addition, we extend the F test to nonlinear models estimated by generalized method of moments and maximum likelihood. Asymptotic critical values that are valid as n approaches inﬁnity with m ﬁxed are provided using a subsampling-like method. The results apply quite generally to processes that are strictly stationary and ergodic under the null hypothesis of no structural instability.
Andrews, Donald W.K., "End-of-Sample Instability Tests" (2002). Cowles Foundation Discussion Papers. 1633.