Ordinary least squares (OLS) is well known to produce an inconsistent estimator of the spatial parameter in pure spatial autoregression (SAR). This paper explores the potential of indirect inference to correct the inconsistency of OLS. Under broad conditions, it is shown that indirect inference (II) based on OLS produces consistent and asymptotically normal estimates in pure SAR regression. The II estimator used here is robust to departures from normal disturbances and is computationally straightforward compared with quasi maximum likelihood (QML). Monte Carlo experiments based on various specifications of the weight matrix show that: (i) the indirect inference estimator displays little bias even in very small samples and gives overall performance that is comparable to the QML while raising variance in some cases; (ii) indirect inference applied to QML also enjoys good finite sample properties; and (iii) indirect inference shows robust performance in the presence of heavy tailed error distributions.

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People with vision impairment and multiple disabilities (MDVI) constitute a population with an enormous heterogeneity due to the combination of various disabilities. Education of children with MDVI concerns different domains and it is considered one of the most demanding fields in Special Education as each student with MDVI has different needs and entails unique educational challenges. In specific, the development of communication skills of children with MDVI is a fundamental domain of their education which affects their learning and active participation in all sectors of life. As a result, professionals are very concerned in which way they can design and develop effective intervention programmes, in order to assess accurately children's communication levels and in turn set realistic goals to consolidate and advance them. The present paper refers to an Erasmus+ project entitled "Promoting effective communication for Individuals with a Vision Impairment and Multiple Disabilities" (PrECIVIM) which acknowledges the need to train teachers in this field and bridges assessment and intervention for the development of communication skills for children with MDVI. The authors present and describe in this paper the following: a. the structure and the content of a training manual for the enhancement of teachers' and professionals' competences in communication skills of children with MDVI, and b. the training process, based on the developed training manual, in three countries (Greece, Cyprus and Romania) in different educational settings for children with MDVI. The obtained data regarding teachers' and professionals' feedback from the training process and their corresponding intervention programmes, revealed a range of good practices as well as concerns and challenges confirming the need of more focused training programmes regarding the education of children with MDVI. The authors conclude that the implementation of intervention programmes, when it comes to communication and levels of communication, depends significantly by a number of factors such as diagnosis, early intervention, system of support for professionals and families, assessment, teachers and professionals' competences, effective use of technology, alternative and augmentative communication, environment, and so on. Finally, it is argued that the aforementioned issues, should be an integral part of a systematic educational policy for the provision of educational opportunities in terms of equality and inclusion for all children including children with MDVI.

Spatial units typically vary over many of their characteristics, introducing potential unobserved heterogeneity which invalidates commonly used homoskedasticity conditions. In the presence of unobserved heteroskedasticity, standard methods based on the (quasi-)likelihood function generally produce inconsistent estimates of both the spatial parameter and the coefficients of the exogenous regressors. A robust generalized method of moments estimator as well as a modified likelihood method have been proposed in the literature to address this issue. The present paper constructs an alternative indirect inference approach which relies on a simple ordinary least squares procedure as its starting point. Heteroskedasticity is accommodated by utilizing a new version of continuous updating that is applied within the indirect inference procedure to take account of the parametrization of the variance-covariance matrix of the disturbances. Finite sample performance of the new estimator is assessed in a Monte Carlo study and found to offer advantages over existing methods. The approach is implemented in an empirical application to house price data in the Boston area, where it is found that spatial effects in house price determination are much more significant under robustification to heterogeneity in the equation errors.

This paper considers the specification and performance of jackknife estimators of the autoregressive coefficient in a model with a near-unit root. The limit distributions of sub-sample estimators that are used in the construction of the jackknife estimator are derived, and the joint moment generating function (MGF) of two components of these distributions is obtained and its properties explored. The MGF can be used to derive the weights for an optimal jackknife estimator that removes fully the first-order finite sample bias from the estimator. The resulting jackknife estimator is shown to perform well in finite samples and, with a suitable choice of the number of sub-samples, is shown to reduce the overall finite sample root mean squared error, as well as bias. However, the optimal jackknife weights rely on knowledge of the near-unit root parameter and a quantity that is related to the long-run variance of the disturbance process, which are typically unknown in practice, and so, this dependence is characterised fully and a discussion provided of the issues that arise in practice in the most general settings.