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CHAPTER 9: SERIAL CORRELATION

CHAPTER 9: SERIAL CORRELATION Page 1 of 19 SERIAL CORRELATION (or autocorrelation) is the violation of Assumption 4 (observations of the error term are uncorrelated with each other). Pure SERIAL CORRELATION This type of CORRELATION tends to be seen in time series data. To denote a time series data set we will use a subscript. This type of SERIAL CORRELATION occurs when the error in one period is correlated with the errors in other periods. The model is assumed to be correctly specified. The most common form of SERIAL CORRELATION is called first-order SERIAL CORRELATION in which the error in time is related to the previous ( 1 period s error: , 1 1 The new parameter is called the first-order autocorrelation coefficient.)

Serial correlation causes OLS to no longer be a minimum variance estimator. 3. Serial correlation causes the estimated variances of the regression coefficients to be biased, leading to unreliable hypothesis testing. The t-statistics will actually appear

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