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...

In this poper we present a consistent spacification test of a parametric regression function against a general nonparametric alternative. The proposed test is based on wavelet estimation ant it is shown to have similar rates of convergence to the more commonly used kernel based tests.

This paper aims to study whether a simple asymetric adjustment costs model with heterogeneity can account for French aggregate job flows.

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.