MTH-416, REGRESSION ANALYSIS Lecture Notes - Lecture 11: Observational Error, Positive-Definite Matrix, Bias Of An Estimator
21 Jul 2021
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One of the basic assumptions in the linear regression model is that the random error components or disturbances are identically and independently distributed. It is assumed that u y x. 0 s s i. e. , the correlation between the successive disturbances is zero. Is violated, i. e. , the variance of disturbance term does not t s t s. Is violated, i. e. , remain constant, then the problem of heteroskedasticity arises. When the variance of disturbance term remains constant though the successive disturbance terms are correlated, then such problem is termed as the problem of autocorrelation. When autocorrelation is present, some or all off-diagonal elements in. Sometimes the study and explanatory variables have a natural sequence order over time, i. e. , the data is collected with respect to time. The disturbance terms in time series data are serially correlated. The autocovariance at lag s is defined as. At zero lag, we have constant variance, i. e. , The autocorrelation coefficient at lag s is defined as.
