This paper presnets a method for simultaneously estimating a system of nonparametric multiple regressions which may seem unrelated, but where the errors are potentially correlated between equations. We show that the prime advantage of estimating such a 'seemingly unrelated' system of...

The half-life is defined as the number of periods required for the impulse response to a unit shock to a time series to dissipate by half. It is widely used as a measure of persistence, especially in international economics to quantify the degree of mean reversion of the deviation from an...

A Bayesian Markov Chain Monte Carlo methodology is developed for estimating the stochastic conditional duration model. The conditional mean of durations between trades is modelled as a latent stochastic process, with the conditional distribution of durations having positive support. The sampling...

In this paper we will investigate the consequences of applying the sieve bootstrap under regularity conditions that are sufficiently general to encompass both fractionally integrated and non-invertible processes. The sieve bootstrap is obtained by approximating the data generating process by an...

This paper demonstrates the application of Bayesian simulation-based estimation to a class of interest rate models known as Affine Term Structure (ATS) models. The technique used is based on a Markov Chain Monte Carlo algorithm, with the discrete observations on yields augmented by additional...

The problem considered in this paper is how to find reliable prediction intervals with simple exponential smoothing and trend corrected exponential smoothing. Methods for constructing prediction intervals based on linear approximation and bootstrapping are proposed.

This paper derives six different forms of message length functions for general linear regression model. In so doing, two different prior densities and the idea of parameter orthogonality are employed.

It is well known that the usual techniques for estimating random and fixed effects panel data models are inconsistent in the dynamic setting. As a consequence, numerous consistent estimators have been proposed in the literature. However, all such estimators rely on certain well defined...

This paper is concerned with model selection based on penalized maximized log likelihood function. Its main emphasis is on how these penalities might be chosen in small samples to give good statistical properties.

Two types of Brownian motion functionals, both time-homogeneous and time-inhomogeneous, are expanded in terms of orthonormal bases in respective Hilbert spaces. Meanwhile, different time horizons are treated from the applicability point of view. Moreover, the degrees of approximation of...