This paper introduces a novel multivariate composite estimator for the Labour Force Survey (LFS). Unlike the univariate composite estimators used in some countries, the multivariate estimator takes into account the different probabilities of transitioning between labour market categories, such...

In this paper, we analyze the nonparametric part of a partially linear model when the covariates in parametric and non-parametric parts are subject to measurement errors. Based on a two-stage semi-parametric estimate, we construct a uniform con dence surface of the multivariate function for...

In this paper, we compare two fundamentally different judgmental demand forecasting approaches used to estimate demand and their corresponding demand distributions. In the first approach, parameters are obtained from a linear regression and maximum likelihood estimation (MLE) based on team...

In observational studies, confounding variables that affect both the exposure and an outcome of interest are a general concern. It is well known that failure to control for confounding variables adequately can worsen inference on an exposure's effect on outcome. In this paper, we explore how...

Better understanding the geography of women's labor market outcomes within countries is important to inform targeted efforts to increase women's economic empowerment. This paper assesses the extent to which a method that combines simulated survey data from urban areas in Mexico with broadly...

Item non-response occurs when respondents fail to provide answers to some or all of the questions posed during survey interviews. The standard procedure is to exclude such responses from the econometric analysis. This may be appropriate if the sample included does not differ significantly from...

This paper reworks and expands on the results of existing simulation studies, investigating the performance of various robust estimators of scale for Tukey's three corner distributions. We focus attention on the popular biweight A-estimator, but also propose a new estimator based on the...

We propose a generic Markov Chain Monte Carlo (MCMC) algorithm to speed up computations for datasets with many observations. A key feature of our approach is the use of the highly efficient difference estimator from the survey sampling literature to estimate the log-likelihood accurately using...

We propose a generic Markov Chain Monte Carlo (MCMC) algorithm to speed up computations for datasets with many observations. A key feature of our approach is the use of the highly efficient difference estimator from the survey sampling literature to estimate the log-likelihood accurately using...