(2)With a small sample the problem of degree of freedom If we introduce too many lagged values, we will lose the same number of the degrees of freedom, plus the intercept and current value. As the number of degrees of freedom dwindles. statistical inference becomes increasingly less reliable. If we have more than one explanatory variable in the model, for every coefficient estimated, we will lose 1 d.f. The degrees of freedom can be consumed even faster
（2）With a small sample～the problem of degree of freedom If we introduce too many lagged values, we will lose the same number of the degrees of freedom, plus the intercept and current value. As the number of degrees of freedom dwindles, statistical inference becomes increasingly less reliable. If we have more than one explanatory variable in the model, for every coefficient estimated, we will lose 1 d.f. The degrees of freedom can be consumed even faster
(3) With a large sample w the problem of multicollinearity and the wrong sign of the coefficientestimated multicollinearity leads to imprecise estimation that is. the standard errors tend to be large. and the t ratios tends to be small Coefficients of successive lagged terms sometimes alternate in sign
（3）With a large sample ～the problem of multicollinearity and the wrong sign of the coefficient estimated. ·Multicollinearity leads to imprecise estimation; that is, the standard errors tend to be large, and the t ratios tends to be small. ·Coefficients of successive lagged terms sometimes alternate in sign