Linear Regression Under Multiple
نویسندگان
چکیده
This dissertation studies the least squares estimator of a trend parameter in a simple linear regression model with multiple changepoints when the changepoint times are known. The error component in the model is allowed to be autocorrelated. The least squares estimator of the trend and the variance of the trend estimator are derived. Consistency and asymptotic normality of the trend estimator are established under wide generality. The Lund et al. (2001) temperature trend study of the contiguous 48 United States is updated as an application. Index words: Ordinary Least Squares, Trend Estimate, Autocorrelation, Consistency, Asymptotic Normality, Temperature Trends, Periodic Time Series, Head-Banging Algorithm Linear Regression Under Multiple Changepoints
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