We evaluate the usefulness of bias-correction methods for autoregressive (AR) models in terms of out-of-sample forecast accuracy, employing two popular methods proposed by Hansen (1999) and So and Shin (1999). Our Monte Carlo simulations show that these methods do not necessarily achieve better...

The least squares (LS) estimator suffers from signicant downward bias in autoregressive models that include an intercept. By construction, the LS estimator yields the best in-sample fit among a class of linear estimators notwithstanding its bias. Then, why do we need to correct for the bias? To...

For panels of possible cross-sectional and serial dependency, stationary bootstrapping is applied to construct unit root tests that are valid regardless of the nuisance parameters of such dependency. The tests are semiparametric in that no model structure is imposed on the serial correlation and...

We evaluate the usefulness of bias-correction methods for autoregressive (AR) models in enhancing the out-of-sample forecast accuracy. We employ two popular methods, proposed by Hansen (1999) and So and Shin (1999). Our Monte Carlo simulations show that these methods do not necessarily achieve...