It is well documented in the literature that the sample skewness and excess kurtosis can be severely biased in finite samples. In this paper, we derive analytical results for their finite-sample biases up to the second order. In general, the bias results depend on the cumulants (up to the sixth...

We derive a neat and compact representation of the asymptotic Fisher information matrix of a vector ARMA process. Its inverse can be used immediately as the asymptotic covariance matrix of the Gaussian maximum likelihood estimator. We also provide the robust sandwich covariance estimator when...

Spatial econometric analyses abound in spatial sciences such as regional and urban economics, geography, and planning. Although the origins of spatial econometrics can be largely traced back to the disciplines of economics and geography, the field of application has by now stretched out as far...

A compact analytical representation of the asymptotic covariance matrix, in terms of model parameters directly, of the quasi maximum likelihood estimator (QMLE) is derived in ARMA models with possible non-zero means and non-Gaussian error terms. For model parameters excluding the error variance,...

We investigate the predictive performance of various classes of value-at-risk (VaR) models in several dimensions — unfiltered versus filtered VaR models, parametric versus nonparametric distributions, conventional versus extreme value distributions, and quantile regression versus inverting the...

In this paper we discuss how to compare various (possibly misspecified) density forecast models using the Kullback-Leibler information criterion (KLIC) of a candidate density forecast model with respect to the true density. The KLIC differential between a pair of competing models is the...

We study the properties of the multi-period-ahead least-squares forecast for the stationary AR(1) model under a general error distribution. We find that the forecast is unbiased up to O(T^(−1)), where T is the in-sample size, regardless of the error distribution and that the mean squared...

I derive the approximate bias and mean squared error of the least squares estimator of the autoregressive coefficient in a stationary first-order dynamic regression model, with or without an intercept, under a general error distribution. It is shown that the effects of nonnormality on the...

We study the finite-sample bias and mean squared error, when properly defined, of the sample coefficient of variation under a general distribution. We employ a Nagar-type expansion and use moments of quadratic forms to derive the results. We find that the approximate bias depends on not only the...

We derive the exact distribution of the maximum likelihood estimator of the mean reversion parameter (k) in the Ornstein-Uhlenbeck process by employing numerical integration via analytical evaluation of a joint characteristic function. Different scenarios are considered: known or unknown drift...