This article proposes omnibus specification tests of parametric dynamic quantile regression models. Contrary to the existing procedures, we allow for a flexible and general specification framework where a possibly continuum of quantiles are simultaneously specified. This is the case for many...

This paper considers semi-parametric frequency domain inference for seasonal or cyclical time series with asymmetric long memory properties. It is shown that tapering the data reduces the bias caused by the asymmetry of the spectral density at the cyclical frequency. We provide a joint treatment...

Using USD bilateral exchange rates in 1975-2009, we find that the strong predictability of foreign excess returns documented in the literature is mainly driven by a particular sample period. We first show that both the statistically significant positive serial dependence of excess returns in the...

We consider cointegration rank estimation for a p-dimensional Fractional Vector Error Correction Model. We propose a new two-step procedure which allows testing for further long-run equilibrium relations with possibly different persistence levels. The first step consists in estimating the...

The forward discount puzzle refers to the robust empirical finding that foreign excess returns are predictable. We investigate if expectations errors are the main cause of this predictability using the serial dependence pattern of excess returns implied by economic models as identification...

This paper investigates, both in finite samples and asymptotically, statistical inference on predictive regressions where time series are generated by present value models of asset prices. We show that regression-based tests, including optimal robust tests such as Jasson and Moreira's...

This paper develops new inference methods for testing the expectations hypothesis in a general econometric framework. In particular, we consider nonparametric tests of the predictability of excess returns in the presence of MA disturbances. We discuss several alternatives of aggregation and...