This paper 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  ends of the finite sample period, regardless of the true break location. I suggest a modification of the least-squares objective function to solve this problem . 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 a small break magnitude . A limit distribution is provided under a in-fill asymptotic framework which verifies that the new estimator outperforms the least-squares estimator  .
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 .
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