The McGraw-Hill Series Economics essentials of economics brue, McConnell, and Flynn Essentials of Economics

spatial autocorrelation,
that is, correlation in space rather than over time. However,
it is important to remember that, in cross-sectional analysis, the ordering of the data must
have some logic, or economic interest, to make sense of any determination of whether
(spatial) autocorrelation is present or not.
The situation, however, 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 intercorrelations, especially if the time interval between
successive observations is short, such as a day, a week, or a month rather than a year. If you
observe stock price indexes, such as the Dow Jones or S&P 500, over successive days, it is
not unusual to find that these indexes move up or down for several days in succession.
Obviously, in situations like this, the assumption of 
no auto-,
or
serial, correlation
in the
error terms that underlies the CLRM will be violated.
In this chapter we take a critical look at this assumption with a view to answering the
following questions:
1. What is the nature of autocorrelation?
2. What are the theoretical and practical consequences of autocorrelation?
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Chapter 12
Autocorrelation: What Happens If the Error Terms Are Correlated?
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3. Since the assumption of no autocorrelation relates to the unobservable disturbances 
u
t
,
how does one know that there is autocorrelation in any given situation? Notice that we
now use the subscript 
t
to emphasize that we are dealing with time series data.
4. How does one remedy the problem of autocorrelation?
The reader will find this chapter in many ways similar to the preceding chapter on het-
eroscedasticity in that