Peter Reinhard Hansen

TL;DR: The paper revisits the inflation forecasting problem posed by Stock and Watson (1999), and compute the model confidence set (MCS) for their set of inflation forecasts, and compares a number of Taylor rule regressions to determine the MCS of the best in terms of in-sample likelihood criteria.

TL;DR: In this article, realised kernels are used to carry out efficient feasible inference on the expost variation of underlying equity prices in the presence of simple models of market frictions, where the weights can be chosen to achieve the best possible rate of convergence and to have an asymptotic variance which is close to that of the maximum likelihood estimator in the parametric version of this problem.

TL;DR: The authors compare 330 ARCH-type models in terms of their ability to describe the conditional variance and find no evidence that a GARCH(1,1) is outperformed by more sophisticated models in their analysis of exchange rates.

TL;DR: In this article, a new test for superior predictive ability is proposed, which is more powerful and less sensitive to poor and irrelevant alternatives than the Reality Check (RC) for data snooping.

TL;DR: The authors compare 330 ARCH-type models in terms of their ability to describe the conditional variance and find no evidence that a GARCH(1,1) is outperformed by more sophisticated models in their analysis of exchange rates.