A well known result is that the Gaussian log-likelihood can be expressed as the sum over different frequency components. This implies that the likelihood ratio statistic has a similar linear decomposition. We exploit these observations to devise diagnostic methods that are useful for...

This paper introduces a nonparametric Granger-causality test for covariance stationary linear processes under, possibly, the presence of long-range dependence. We show that the test is consistent and has power against contiguous alternatives converging to the parametric rate T-½. Since the test...

Contrary to the classic framework of passive strategies, if investors exploit return predictability through active strategies then there is a tension between the mean-variance frontiers that drive empirical work and the mean-variance preferences that are used in finance theory. We show that...

This paper introduces a new class of parameter estimators for dynamic models, called Simulated Nonparametric Estimators (SNE). The SNE minimizes appropriate distances between nonparametric joint (or conditional) densities estimated from sample data and nonparametric joint (or conditional)...

This paper introduces a new parameter estimator of dynamic models in which the state is a multidimensional, continuous-time, partially observed Markov process. The estimator minimizes appropriate distances between nonparametric joint (and/or conditional) densities of sample data and...

This paper uses recently developed methods for estimating dynamic heterogeneous cointegrated panel data models - which allows for heterogeneity in parameters and dynamics across agents - to study housing wealth effects in a dynamic model of the 50 US states and the District of Columbia from the...

There is a growing literature on unit root testing in threshold autoregressive models. This paper makes two contributions to the literature. First, an asymptotic theory is developed for unit root testing in a threshold autoregression, in which the errors are allowed to be dependent and...