Value-at-Risk (VaR) has become a standard measure for risk management and regulation. In the case of a two-parameter distribution, a common method among practitioners is first to calculate the daily VaR and then to apply it to a longer investment horizon by using the Square Root Rule (SRR). We...

This paper considers various asymptotic approximations in the near-integrated firstorder autoregressive model with a non-zero initial condition. We first extend the work of Knight and Satchell (1993), who considered the random walk case with a zero initial condition, to derive the expansion of...

In this paper we have proposed a method of estimating a set of regional price index numbers from a given household level data set on item-wise unit values/prices.

In this paper, we study maximum likelihood estimation and Lagrange multiplier testing of a one-way error components regression model suitable for incomplete panel and including parametrically specified variance functions for both individual-specific and general error disturbances.

A geometrical setting is constructed, based on Hilbert space, in which the asymptotic properties of estimators can be studied. Estimators are defined in the context of parametrised models, which are treated as submanifolds of an underlying Hilbert manifold, on which a parameter-defining mapping...

The minimum Covariance Determinant (MCD) scatter estimator is a highly robust estimator for the dispersion matrix of a multivariate, elliptically symmetric distribution. It is fast to compute and intuitively appealing. In this note we derive its influence function and compute the asymptotic...

This paper proposes alternative methods for constructing estimators from accept-reject samples by incorporating the variables rejected by the algorithm.

This paper synthesises a global approach to both Bayesian and likelihood treatments of the estimation of the parameters of a hidden Markov model for the cases of normal and Poisson underlying distribution.

This paper puts forth a concept of Adptivety Rational Equilibrium (A.R.E) where agents base decisions upon predictions of future values of endogenous variables whose actual values are determined by equilibrium equations.