I derive the approximate bias and mean squared error of the least
squares estimator of the autoregressive coefficient in a stationary
first-order dynamic regression model, with or without an intercept, under
a general error distribution. It is shown that the effects of nonnormality
on the approximate moments of the least squares estimator come into play
through the skewness and kurtosis coefficients of the nonnormal error
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