  This study proposes a point estimator of the break location for a one-time
structural break in linear regression models. If the break magnitude is small,
the least-squares estimator of the break date has two modes at the ends of the
finite sample period, regardless of the true break location. To solve this
problem, I suggest an alternative estimator based on a modification of the
least-squares objective function. The modified objective function incorporates
estimation uncertainty that varies across potential break dates. The new break
point estimator is consistent and has a unimodal finite sample distribution
under small break magnitudes. A limit distribution is provided under an in-fill
asymptotic framework. Monte Carlo simulation results suggest that the new
estimator outperforms the least-squares estimator. I apply the method to
estimate the break date in U.S. real GDP growth and U.S. and UK stock return
prediction models.
