Assessing Variability of Complex Descriptive Statistics in Monte Carlo Studies Using Resampling Methods

Good statistical practice dictates that summaries in Monte Carlo studies should always be accompanied by standard errors. Those standard errors are easy to provide for summaries that are sample means over the replications of the Monte Carlo output: for example, bias estimates, power estimates for tests and mean squared error estimates. But often more complex summaries are of interest: medians (often displayed in boxplots), sample variances, ratios of sample variances and non‐normality measures such as skewness and kurtosis. In principle, standard errors for most of these latter summaries may be derived from the Delta Method, but that extra step is often a barrier for standard errors to be provided. Here, we highlight the simplicity of using the jackknife and bootstrap to compute these standard errors, even when the summaries are somewhat complicated. © 2014 The Authors. International Statistical Review © 2014 International Statistical Institute     

The Obama effect

Many observers argued that Barack Obama’s candidacy in the U.S. presidential election of 2008 benefited from the financial crisis and recessionary economic conditions which voters blamed on the Republican administration. However, an empirical examination of stock price and public opinion data indicates that improvements in Obama’s electoral prospects led stock price declines and that gains by Obama were more likely to be followed by falling stock prices than by rising prices. This evidence suggests that the poor performance of the stock market in the days leading up to Obama’s victory was partly caused by, rather than causing, Obama’s success.

More than a feeling: using hotel room attendants to improve understanding of job quality

Recent research by Adler and Adler reveals contradictory claims about the job quality of hotel room attendants; suggesting that an objectively ‘bad’ job can be perceived as subjectively ‘good’ by workers. This contradiction resonates with wider issues about how job quality is conceived – objectively and/or subjectively. Drawing on empirical research of room attendant jobs in upper market hotels in three cities in the UK and Australia, this paper addresses the contradiction by examining both the objective and subjective dimensions of job quality for room attendants. In doing so it refines Adler and Adler's work, constructs a new typology of workers and a new categorisation of job quality informed by workers characteristics and preferences. This categorisation improves conceptual understanding of job quality by enjoining its objective and subjective dimensions.     

Not part of the family? The limits to managing the corporate way in international hotel chains

Companies increasingly seek solutions to the corporate/local dichotomies perceived to be a feature of more traditional approaches to managing across national boundaries. At the human resource level, the rhetoric of transnationalism emphasizes integration being achieved through 'soft' mechanisms, such as corporate culture devices, which encourage all managers to develop an international (for this read corporate) perspective on what they do. In theory, managerial staff are recruited and promoted on a 'best person for the job' basis and national identities are played down. Drawing upon evidence from three international hotel chains (one American, one French and one Swedish), this paper argues that there is a disjuncture between corporate culture devices which assume that they can transcend national origins and the issues of interest and identity which inform the activities and experiences of managers at unit level. The paper suggests that companies need to be aware of the danger of assuming that one can be trained to be 'one of the family'. Rather, we argue that local managers are potentially disadvantaged in terms of career progression as managers from the parent country utilize criteria of acceptability informed by processes of socialization which are more institutionally embedded and derived than has been assumed.     

Macroeconomic forecasting using structural factor analysis

The use of a small number of underlying factors to summarize the information from a much larger set of information variables is one of the new frontiers in forecasting. In prior work, the estimated factors have not usually had a structural interpretation and the factors have not been chosen on a theoretical basis. In this paper we propose several variants of a general structural factor forecasting model, and use these to forecast certain key macroeconomic variables. We make the choice of factors more structurally meaningful by estimating factors from subsets of information variables, where these variables can be assigned to subsets on the basis of economic theory. We compare the forecasting performance of the structural factor forecasting model with that of a univariate AR model, a standard VAR model, and some non-structural factor forecasting models. The results suggest that our structural factor forecasting model performs significantly better in forecasting real activity variables, especially at short horizons.