Disclosure‐protected Inference Using Generalised Linear Models

Vast amounts of data that could be used in the development and evaluation of policy for the benefit of society are collected by statistical agencies. It is therefore no surprise that there is very strong demand from analysts, within business, government, universities and other organisations, to access such data. When allowing access to micro‐data, a statistical agency is obliged, often legally, to ensure that it is unlikely to result in the disclosure of information about a particular person or organisation. Managing the risk of disclosure is referred to as statistical disclosure control (SDC). This paper describes an approach to SDC for output from analysis using generalised linear models, including estimates of regression parameters and their variances, diagnostic statistics and plots. The Australian Bureau of Statistics has implemented the approach in a remote analysis system, which returns analysis output from remotely submitted queries. A framework for measuring disclosure risk associated with a remote server is proposed. The disclosure risk and utility of approach are measured in two real‐life case studies and in simulation.     

Fair Value Measurements of Control Premiums

As the overview of the current state of research within this paper shows, the debate around fair value measurements is far from over. This paper analyzes fair value measurement requirements in a controversial scenario, namely when a control premium exists. The analyses of the paper show that, while measurement rules around control premiums could have a material impact on fair value measurements and the financial statements as a whole, significant fair value measurement issues remain unresolved. The conclusion is that fair value measurements should include or exclude control premiums consistently. It is argued that including control premiums for all fair value measurements is the most faithful representation of the underlying economic phenomenon. This paper contributes to the fair value measurement debate by comparing the merits of alternative fair value measurements for control premiums and highlights an area where researchers, investors, and other users should exercise caution when evaluating financial statements.     

RIDGE ESTIMATORS FOR PROBIT REGRESSION: WITH AN APPLICATION TO LABOUR MARKET DATA

In this paper we propose ridge regression estimators for probit models since the commonly applied maximum likelihood (ML) method is sensitive to multicollinearity. An extensive Monte Carlo study is conducted where the performance of the ML method and the probit ridge regression (PRR) is investigated when the data are collinear. In the simulation study we evaluate a number of methods of estimating the ridge parameter k that have recently been developed for use in linear regression analysis. The results from the simulation study show that there is at least one group of the estimators of k that regularly has a lower mean squared error than the ML method for all different situations that have been evaluated. Finally, we show the benefit of the new method using the classical Dehejia and Wahba dataset which is based on a labour market experiment.     

Social and economic problems of the older generation: Quality of life of the older population

The first part of this published paper has provided a brief assessment of the demographic situation in the world and considers the problems of the health protection of elderly age groups and ensuring of their level of income and material well-being through the state pension system and their own work activities, as well as measures for protecting the interests of elderly people in the consumer sector. The shortcomings of the adopted strategy of action for the benefit of the older generation in the Russian Federation until 2025 have been discussed. The second part of this article will be be published in a forthcoming issue of this journal.