Who Settles for Less? Subjective Dispositions,Objective Circumstances,and Housing Satisfaction

In recent years there has been growing interest in individuals’ self-perceptions of their wellbeing on the grounds that these complement well-established objective indicators of welfare. However, individuals’ assessments depend on both objective circumstances and subjective, idiosyncratic dispositions, such as aspirations and expectations. We add to the literature by formulating a modelling strategy that uncovers how these subjective dispositions differ across socio-demographic groups. This is then tested using housing satisfaction data from a large-scale household panel survey from Australia. We find that there are significant differences in the way in which individuals with different characteristics rate the same objective reality. For instance, male, older, migrant, and Indigenous individuals rate equal housing conditions more favourably than female, younger, Australian-born, and non-Indigenous individuals. These findings have important implications for how self-reported housing satisfaction, and wellbeing data in general, are to be used to inform evidence-based policy.     

Simulation study of confidence regions for canonical variate analysis

Canonical variate analysis often involves the construction of confidence regions round points representing group means in a 2-dimensional plot. Traditionally circles have always been constructed, but some authors have recently advocated ellipses as being more appropriate. This paper describes a Monte Carlo study investigating the effect of a range of factors on the inclusion rates of true population means within both types of region for normal data. The traditional circles do not perform too badly within a restricted range, but they are nearly always under-included. The ellipses usually have higher inclusion rates, and so are often closer to the nominal rate, but are sometimes over-included.     

ON SELECTING VARIABLES AND ASSESSING THEIR PERFORMANCE IN LINEAR DISCRIMINANT ANALYSIS

Linear discriminant analysis between two populations is considered in this paper. Error rate is reviewed as a criterion for selection of variables, and a stepwise procedure is outlined that selects variables on the basis of empirical estimates of error. Problems with assessment of the selected variables are highlighted. A leave-one-out method is proposed for estimating the true error rate of the selected variables, or alternatively of the selection procedure itself. Monte Carlo simulations, of multivariate binary as well as multivariate normal data, demonstrate the feasibility of the proposed method and indicate its much greater accuracy relative to that of other available methods.     

Exploratory factor and principal component analyses: some new aspects

Exploratory Factor Analysis (EFA) and Principal Component Analysis (PCA) are popular techniques for simplifying the presentation of, and investigating the structure of, an (n×p) data matrix. However, these fundamentally different techniques are frequently confused, and the differences between them are obscured, because they give similar results in some practical cases. We therefore investigate conditions under which they are expected to be close to each other, by considering EFA as a matrix decomposition so that it can be directly compared with the data matrix decomposition underlying PCA. Correspondingly, we propose an extended version of PCA, called the EFA-like PCA, which mimics the EFA matrix decomposition in the sense that they contain the same unknowns. We provide iterative algorithms for estimating the EFA-like PCA parameters, and derive conditions that have to be satisfied for the two techniques to give similar results. Throughout, we consider separately the cases n>p and p≥n. All derived algorithms and matrix conditions are illustrated on two data sets, one for each of these two cases.     

FOREIGN BANKS AND THE BANK LENDING CHANNEL

We provide new evidence on bank ownership and transmission of monetary policy using bank‐level data on 453 banks in Central and Eastern European economies between 1998 and 2012. Only domestic banks adjust loans to changes in monetary policy, while foreign banks do not. Conventional wisdom says that this is because foreign banks can rely on parent banks' funding to insulate against monetary policy shocks. In this paper we document an alternative explanation. Deposits in foreign banks do not react to monetary policy, hence the bank lending channel is only triggered in domestic banks. (JEL E50, F36, G21)     

The effects of data input errors on subsequent statistical inference

Data input errors can potentially affect statistical inferences, but little research has been published to date on this topic. In the present paper, we report the effect of data input errors on the statistical inferences drawn about population parameters in an empirical study involving 280 students from two Polish universities, namely the Warsaw University of Life Sciences – SGGW and the University of Information Technology and Management in Rzeszow. We found that 28% of the students committed at least one data error. While some of these errors were small and did not have any real effect, a few of them had substantial effects on the statistical inferences drawn about the population parameters.     

An alternative measure of social wellbeing: analysing the key conceptual and statistical components of quality of life

Statisticians, policy makers and social researchers widely accept that there is a need to consider a more nuanced range of measures of quality of life that move beyond the economic domain and that take into account key aspects of an individual's life circumstances. Using data from an Australian household survey, a composite Wellbeing Index was created that covered objective circumstances, with known associations to wellbeing, evaluated from the individual's subjective viewpoint. The robustness of the measure comes from the fact that while covering a broad range of key dimensions, the index only includes the items deemed important components of wellbeing by a majority of respondents. The index was then used to explore the extent to which wellbeing is associated with other dimensions of quality of life that have currency in the contemporary literature. The study contributes to the contemporary debate on social wellbeing and adds new Australian evidence to a body of research that has been mainly based on European and American data.     

Data-based interval estimation of classification error rates

Leave-one-out and 632 bootstrap are popular data-based methods of estimating the true error rate of a classification rule, but practical applications almost exclusively quote only point estimates. Interval estimation would provide better assessment of the future performance of the rule, but little has been published on this topic. We first review general-purpose jackknife and bootstrap methodology that can be used in conjunction with leave-one-out estimates to provide prediction intervals for true error rates of classification rules. Monte Carlo simulation is then used to investigate coverage rates of the resulting intervals for normal data, but the results are disappointing; standard intervals show considerable overinclusion, intervals based on Edgeworth approximations or random weighting do not perform well, and while a bootstrap approach provides intervals with coverage rates closer to the nominal ones there is still marked underinclusion. We then turn to intervals constructed from 632 bootstrap estimates, and show that much better results are obtained. Although there is now some overinclusion, particularly for large training samples, the actual coverage rates are sufficiently close to the nominal rates for the method to be recommended. An application to real data illustrates the considerable variability that can arise in practical estimation of error rates.