文档介绍：Chapter12
Autocorrelation:
What Happens
if Error Terms are Correlated
The Nature of Autocorrelation
(1) CLRM assumption:
No autocorrelation exist in dishurbances μi;
E(μiμj)= 0 i≠j
Autocorrelation means: E(μiμj)≠0 i≠j
(2)Autocorrelation is usually associated with time series data, but it can also occur in cross-sectional data, which is called spatial correlation.
(3)Autocorrelation can be positive as well as negative.
2. Patterns of autocorrelation
Figure 12-1, p379
3. Reasons of autocorrelation
(1) Inertia or sluggishness
Most economic time-series is inertia,
such as GDP, money supply, price indexes,
so essive observations are correlated.
(2)Model Specification Error(s)
Some important variables that should be included in the model are not included (underspecification)
The model has the wrong functional form
. a linear-in-variable(LIV) model is fitted whereas a log-linear model should have been fitted.
(3)Cobweb phenomenon
The modities often reflects the Cobweb phenomenon, where supply reacts
to price with a lag of one time period because supply decisions take time to implement, the beginning of this year’s planting of crops farmers are influenced by the price prevailing last year
Supplyt=B1+B2Pt-1+μt
(4)Data Manipulation
Data smoothness can itself lead to a
systematic pattern in the disturbances,
thereby inducing autocorrelation.
Consequences of autocorrelation
(1)The OLS estimators are linear and unbiased
(2)The OLS estimators are not efficient
The error variance of OLS estimators is a biased estimator of the true σ2
The estimated variances sometimes underestimate true variances and standard errors, thereby inflating t values
(3)The t and F tests are not generally reliable.
(4)The puted R2 may be an unreliable measure of true R2.
(5)Variances and standard errors of forecast may also be inefficient.
Detecting Autocorrelation
Because the true ui are unobservable, we
have to rely on the e