12.2 Consequences of autocorrelation (1The 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 o 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 conventionally computed R2 may be an unreliable measure of true r (5) Variances and standard errors of forecast may also be inefficient12.2 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 conventionally computed R2 may be an unreliable measure of true R2 . （5）Variances and standard errors of forecast may also be inefficient