LtChapter12 Autocorrelation What happens if Error Terms are Correlated y
Chapter12 Autocorrelation: What Happens if Error Terms are Correlated
12.1 The Nature of autocorrelation 1. Definition (1) CLRM assumption No autocorrelation exist in dishurbances u E(μy)=0 Autocorrelation means E(41)≠0 1 (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
12.1 The Nature of Autocorrelation 1.Definition （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
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 successive observations are correlated
3. Reasons of autocorrelation (1) Inertia or sluggishness Most economic time-series is inertia, such as GDP, money supply, price indexes, so successive observations are correlated
(2) Model Specification Error(s) . Some important variables that should be included in the model are not included (underspecification) .o The model has the wrong functional form e.g. a linear-in-variable(llv) model is fitted whereas a log-linear model should have been fitted
（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 e.g. a linear-in-variable(LIV) model is fitted whereas a log-linear model should have been fitted