We consider bandwidth selection for the kernel estimator of conditional density with one explanatory variable. Several bandwidth selection methods are derived ranging from fast rules-of-thumb which assume the underlying densities are known to relatively slow procedures which use the bootstrap....

The data consists of multivariate failure times under random censorship. By the kernel smoothing techniques, convolutions of integrated multivariated hazard functions provide some estimators of the so-called multivariate hazard functions (Fermanian (1995)). We adopt the method of Jones, Marron...

In many time series models, an infinite number of moments can be used for estimation in a large sample. I supply a technically undemanding proof of a condition for optimal instrumental variables use of such moments in a parametric model. I also illustrate application of the condition in...

The paper derives and tests maximum likelihood parameter estimators for symmetrically correlated Weiner processes observed at discrete intervals. Such processes are arise when pricing and determining Value-at-Risk for portfolio derivatives. Cases of driftless and mean-reverting state variables...

In this paper we extend the univariate periodic integration model to multivariate cointegrated time series. We analyze representation issues of a multivariate periodic model. We argue that simple adding an index s to the parameters in an otherwise nonperiodic Vector AutoRegression (VAR) leads to...

The paper derives and tests maximum likelihood parameter estimators for symmetrically correlated Weiner processes observed at discrete intervals. Such processes are arise when pricing and determining Value-at-Risk for portfolio derivatives. Cases of driftless and mean-reverting state variables...

A recent addition to the ARCH family of econometric models was introduced by Ding, Granger and Engle (1993) wherein the power term by which the data is transformed was estimated within the model rather than being imposed by the researcher. This paper considers the ability of the Power GARCH...

This paper combines techniques drawn from the literature on evolutionary optimization algorithms along with bootstrap based statistical tests. Bootstrapping is used as a general framework for estimating objectives out of sample by redrawing subsets from a training sample. Evolution is used to...

In our paper, an evaluatee can selectively correct individual performance measures which results in the final evaluation being less accurate.