Suchergebnisse

This paper considers the scenario that all data entries in a confidentialised unit record file were masked by multiplicative noises, regardless of whether unit records are sensitive or not and regardless of whether the masked variables are dependent or independent variables in the underlying regression analysis. A technique is introduced in this paper to show how to estimate parameters in a regression model, which is originally fitted by unmasked data, based on masked data. Several simulation studies and a real-life data application are presented.

We discuss alternative approaches for estimating from cross-sectional categorical data in the presence of
misclassification. Two parameterisations of the misclassification model are reviewed. The first employs misclassification
probabilities and leads to moment-based inference. The second employs calibration probabilities and leads to maximum likelihood inference. We show that maximum likelihood estimation can be alternatively performed by employing misclassification probabilities and a missing data specification. As an alternative to maximum likelihood estimation we propose a quasi-likelihood parameterisation of the misclassification model. In this context an explicit definition of the likelihood function is avoided and a different way of resolving a missing data problem is provided.
Variance estimation for the alternative point estimators is considered. The different approaches are illustrated using real data from the UK Labour Force Survey and simulated data.

Pairs trading is a comparative-value form of statistical arbitrage designed
to exploit temporary random departures from equilibrium pricing between two shares.
However, the strategy is not riskless. Market events as well as poor statistical modeling
and parameter estimation may all erode potential profits. Since conventional loss
limiting trading strategies are costly, a preferable situation is to integrate loss limitation
within the statistical modeling itself. This paper uses cointegration principles to develop
a procedure that embeds a minimum profit condition within a pairs trading strategy.
We derive the necessary conditions for such a procedure and then use them to define and
implement a five-step procedure for identifying eligible trades. The statistical validity of
the procedure is verified through simulation data. Practicality is tested through actual
data. The results show that, at reasonable minimum profit levels, the protocol does
not greatly reduce trade numbers or absolute profits relative to an unprotected trading
strategy.

The study of long-run equilibrium processes is a significant component of economic and finance theory. The Johansen technique for identifying the existence of such long-run stationary equilibrium conditions among financial time series allows the identification of all potential linearly independent cointegrating vectors within a given system of eligible financial time series. The practical application of the technique may be restricted, however, by the pre-condition that the underlying data generating process fits a finite-order vector autoregression (VAR) model with white noise. This paper studies an alternative method for determining cointegrating relationships without such a pre-condition. The method is simple to implement through commonly available statistical packages. This 'residual-based cointegration' (RBC) technique uses the relationship between cointegration and univariate Box-Jenkins ARIMA models to identify cointegrating vectors through the rank of the covariance matrix of the residual processes which result from the fitting of univariate ARIMA models. The RBC approach for identifying multivariate cointegrating vectors is explained and then demonstrated through simulated examples. The RBC and Johansen techniques are then both implemented using several real-life financial time series.

This paper derives a functional limit theorem for general nonstationary
fractionally integrated processes having no influence from prehistory. Asymptotic distributions
of sample autocovariances and sample autocorrelations based on these processes
are also investigated. The problem arises naturally in discussing fractionally integrated
processes when the processes starts at a given initial date.

This paper is concerned with the application of an asymptotic quasi-likelihood
practical procedure to estimate the unknown parameters in linear stochastic models of the form
yt=ft(θ)+Mt(θ)(t=1,2,..,T)
, where ft
is a linear predictable process of θ
and Mt
is an
error term such that E(Mt|Ft−1)=0
and E(Mt2|Ft−1)<∞
and F
is a σ-field generated
from{ys}s≤t
. For this model, to estimate the parameter θ∈Θ, the ordinary least squares
method is usually inappropriate (if there is only one observable path of {yt} and if E(Mt2|Ft−1)
is not a constant) and the maximum likelihood method either does not exist or is mathematically
intractable. If the finite dimensional distribution of Mt
is unknown, to obtain a good estimate of
θ
an appropriate predictable process gt should be determined. In this paper, criteria for determining
gt
are introduced which, if satisfied, provide more accurate estimates of the parameters via the
asymptotic quasi-likelihood method.