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In cross-section, data are often collected on the basis of a random sample of cross-sectional units, such as households or firms so that there is little reason to believe that the error term pertaining to one household or a firm is correlated with the error term of another household or firm.
The situation is likely to be very different if we are dealing with time series data, for the observations in such data follow a natural ordering over time so that successive observations are likely to exhibit inter correlations, especially if the time interval between successive observations is short, such as a day, a week, or a month rather than.
The classical model assumes that the disturbance term relating to any observation is not influenced by the disturbance term relating to any other observation. For example, if we are dealing with quarterly time series data involving the regression of output on labor and capital inputs and there is labor strike affecting output in one quarter, there is no reason to believe that this disruption will be carried over to the next quarter. That is, if output is lower this quarter, there is no reason to expect it to be lower next quarter. Similarly, if we are dealing with cross-sectional data involving regression of family consumption expenditure on family income, the effect of an increase of one family's income on its consumption expenditure is not expected to affect the consumption expenditure of another family. If such dependence exists there exists autocorrelation. Symbolically,
In this situation, the disruption caused by a strike this quarte ...
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