We establish the validity of an Edgeworth expansion to the distribution of the maximum likelihood estimator of the parameter of a stationary, Gaussian, strongly dependent process. The result covers many types of ARFIMA models.

The paper proposes an original class of conditionally heteroskedastic models aimed to capture contemporaneous asymmetry. Not only past up and down moves of stock market returns have different impacts on the conditional variance, but also, positive and negative changes are governed by different...

We take a fresh look at Theil's BLUS residuals and ask why they have gone out of fashion.All our simulation experiments indicate that tests based on BLUS residuals have higher power than those based on the more popular recursive residuals, even in those cases (structural breaks) where intuition...

We propose a Bayesian procedure for multiple outlier detection in linear models avoiding the masking problem. Our proposal is illustrated with several examples in which our procedure outperforms other recent methods for multiple outlier detection. The posterior probalities of each data point...

In this paper the authors study the problem of non parametric estimation of an unknown regression function from dependent data with sub-Gaussian errors. As a particular case, they handle the autoregressive framework.

In many VARs, monetary policy shocks are identified with the least squares residuals from a regression of the federal funds rate on an assortment of variables. Such regressions appear to be structurally fragile and are at odds with other evidence on the nature of the Fed's reaction function;...

This paper studies seemingly unrelated linear models with integrated regressors and stationary errors.

A new technique for estimating market power in several markets simultaneously is developed and applied to the Australian retail beef, lamb, and pork markets. Some support is found for the restriction that market power is the same for each meat. Given equal market power in all three meats, we...