Abstract :
This paper extends the Andrews (2002, Econometrica 71, 1661–1694) and Andrews
and Kim (2006, Journal of Business & Economic Statistics 24, 379–394) ordinary
least squares–based end-of-sample instability tests for linear regression models. The
author proposes to quasi-difference the data first using a consistent estimate of the
sum of the autoregressive coefficients of the error process and then test for the endof-
sample instability. For the cointegration model, the feasible quasi-generalized
least squares (FQGLS) version of the Andrews and Kim (2006) P test is considered
and is shown, by simulations, to be more robust to serial correlation in the error
process and to have power no less than Andrews and Kim’s original test. For the
linear time trend model, the FQGLS version of the Andrews (2002) S test is considered
with the error process allowed to be nonstationary up to one unit root, and the
new test is shown to be robust to potentially nonstationary serial correlation. A simulation
study also shows that the finite-sample properties of the proposed test can be
further improved when the Andrews (1993, Econometrica 61,139–165) or Andrews
and Chen (1994, Journal of Business & Economic Statistics 12, 187–204) median
unbiased estimate of the sum of the autoregressive coefficients is used.
