This paper studies the ordinary least squares trend estimator in a simple linear regression under the setting of multiple known changepoint times. The error component in the model is allowed to be a general short-memory stationary autocorrelated series. Consistency and asymptotic normality of the estimator is established and its limiting properties are quantified. An example in climatology is given where the multiple changepoint aspect is key.
Simple Linear Regression Under Multiple Changepoints