We derive general formulae for the asymptotic distribution of the LIML estimator for the coefficients of both endogenous and exogenous variables in a partially identified linear structural equation. We extend previous results of Phillips (1989) and Choi and Phillips (1992) where the focus was on...

In this paper, we study semiparametric estimation for a single-index panel data model where the nonlinear link function varies among the individuals. We propose using the refined minimum average variance estimation method to estimate the parameter in the single-index. As the cross-section...

In this paper, we study parametric nonlinear regression under the Harris recurrent Markov chain framework. We first consider the nonlinear least squares estimators of the parameters in the homoskedastic case, and establish asymptotic theory for the proposed estimators. Our results show that the...

Modelling stock return generating process as a single factor model, we show analytically that the relation between idiosyncratic volatility measured as variance of the residuals and expected stock return in the cross-section may be represented by a parabola that opens to the left and has...

Several authors have postulated econometric models for exchange rates restricted to lie within known target zones. However, it is not uncommon to observe exchange rate data with known limits that are not fully 'credible'; that is, where some of the observations fall outside the stated range. An...

We study methods for constructing confidence intervals, and confidence bands, for estimators of receiver operating characteristics. Particular emphasis is placed on the way in which smoothing should be implemented, when estimating either the characteristic itself or its variance. We show that...

In this paper we present a test statistic, which will be used to test for significant differences between generating processes of two time series that may be logically connected. The test statistic is based on the differences between estimated parameters of the autoregressive models which are...

In this paper the EM algorithm, which has been used successfully with censored and incomplete data sets, is adapted to the problem of scrambled data. The performance of the method is assayed using an artificially constructed data set. The relevance of the results for a real world labour market...

A semiparametric method is studied for estimating the dependence parameter and the joint distribution of the error term in a class of multivariate time series models when the marginal distributions of the errors are unknown. This method is a natural extension of Genest et al. (1995a) for...

The presence of nuisance parameters causes unexpected complications in econometric inference problems. A number of modified likelihood and message length functions have been developed for better handling of nuisance parameters but all of them are not equally efficient. In this paper, we...