Let be a family of (N,d)-anisotropic Gaussian random fields with generalized Hurst indices H=(H1,...,HN)[set membership, variant](0,1)N. Under certain general conditions, we prove that the local time of is jointly continuous whenever . Moreover we show that, when H approaches H0, the law of the...

Let be the solution process for the following Cauchy problem for the stochastic fractional partial differential equation taking values in : where (1[alpha]3, [delta]=min{[alpha]-[[alpha]],2+[[alpha]]2-[alpha]}) is the fractional differential operator with respect to the spatial variable x (see...

We provide a new asymptotic theory for local time density estimation for a general class of functionals of integrated time series. This result provides a convenient basis for developing an asymptotic theory for nonparametric cointegrating regression and autoregression. Our treatment directly...

Asymptotic theory is developed for local time density estimation for a general class of functionals of integrated and fractionally integrated time series. The main result provides a convenient basis for developing a limit theory for nonparametric cointegrating regression and nonstationary...

Limit theory involving stochastic integrals is now widespread in time series econometrics and relies on a few key results on function space weak convergence. In establishing weak convergence of sample covariances to stochastic integrals, the literature commonly uses martingale and semimartingale...

This paper develops asymptotic theory for a nonlinear parametric cointegrating regression model. We establish a general framework for weak consistency that is easy to apply for various nonstationary time series, including partial sums of linear processes and Harris recurrent Markov chains. We...

This paper studies a nonlinear cointegrating regression model with nonlinear nonstationary heteroskedastic error processes. We establish uniform consistency for the conventional kernel estimate of the unknown regression function and develop atwo-stage approach for the estimation of the...