Comparing Possibly Misspecified Forecasts

Abstract

Recent work has emphasized the importance of evaluating estimates of a statistical functional (such as a conditional mean, quantile, or distribution) using a loss function that is consistent for the functional of interest, of which there is an infinite number. If forecasters all use correctly specified models free from estimation error, and if the information sets of competing forecasters are nested, then the ranking induced by a single consistent loss function is sufficient for the ranking by any consistent loss function. This article shows, via analytical results and realistic simulation-based analyses, that the presence of misspecified models, parameter estimation error, or nonnested information sets, leads generally to sensitivity to the choice of (consistent) loss function. Thus, rather than merely specifying the target functional, which narrows the set of relevant loss functions only to the class of loss functions consistent for that functional, forecast consumers or survey designers should specify the single specific loss function that will be used to evaluate forecasts. An application to survey forecasts of U.S. inflation illustrates the results.     

Assessing Statistical Results: Magnitude,Precision, and Model Uncertainty

ABSTRACT

Evaluating the importance and the strength of empirical evidence requires asking three questions: First, what are the practical implications of the findings? Second, how precise are the estimates? Confidence intervals provide an intuitive way to communicate precision. Although nontechnical audiences often misinterpret confidence intervals (CIs), I argue that the result is less dangerous than the misunderstandings that arise from hypothesis tests. Third, is the model correctly specified? The validity of point estimates and CIs depends on the soundness of the underlying model.     

“Me,the street,and a backpack”: employment,income, and physical capital in rural recovery

ABSTRACT

Recovery capital is a framework for understanding individuals’ resources for recovery from substance misuse. In a qualitative analysis of rural individuals in recovery (N = 49), the role of physical capital emerged prominently across ten focus groups. Participants highlighted the effects of employment status, income, and other forms of physical capital directly, as well as indirect effects through changes in human capital. Negative cultural capital components, in the form of stigma and employment discrimination, were described as barriers to physical capital acquisition and successful recovery. Based on these findings, we discuss rural recovery challenges and propose a model of recovery capital interactions.     

Indigenous research methodology – weaving a research interface

ABSTRACT

Indigenous research Knowledges and methodologies have existed over millennia, however it is only recently that Indigenous scholars have been able to challenge institutional Western hegemony to reclaim sovereignty in the research space. Despite the high volume of quantitative research describing Aboriginal and Torres Strait Islander health, there has been limited evaluation of the value added through incorporation of Indigenous Knowledges and methodologies. ‘Research at the interface’ has been discussed as an Indigenous research methodology for researchers to contextualise and inform their research practices, between Indigenous and Western systems of knowledge. In this article we address the significance of ‘research at the interface’ for Aboriginal and Torres Strait Islander research, as an exciting opportunity for innovation to ensure strength, self-determination and resilience for Aboriginal and Torres Strait Islander communities engaged in research. We also introduce weaving a methodology for ‘research at the interface’ as a process for conceptualising Indigenous and quantitative research methodologies at the interface.     

Detecting Directionality in Time Series

Abstract

Directionality can be seen in many stationary time series from various disciplines, but it is overlooked when fitting linear models with Gaussian errors. Moreover, we cannot rely on distinguishing directionality by comparing a plot of a time series in time order with a plot in reverse time order. In general, a statistical measure is required to detect and quantify directionality. There are several quite different qualitative forms of directionality, and we distinguish: rapid rises followed by slow recessions; rapid increases and rapid decreases from the mean followed by slow recovery toward the mean; directionality above or below some threshold; and intermittent directionality. The first objective is to develop a suite of statistical measures that will detect directionality and help classify its nature. The second objective is to demonstrate the potential benefits of detecting directionality. We consider applications from business, environmental science, finance, and medicine. Time series data are collected from many processes, both natural and anthropogenic, by a wide range of organizations, and directionality can easily be monitored as part of routine analysis. We suggest that doing so may provide new insights to the processes.     

Public sector delivery of waste management services: cases from the Indian Sub-Continent

This paper discusses the role of public sector institutions in the delivery of urban services, with a particular focus on waste management in the Indian Sub-Continent. Public sector institutions are the major stakeholders in the delivery of urban sevices. They hold the major responsibilities, funds and employ a large number of staff. The public sector institutions are also exposed to political influence and the institutional changes are often politically or donors driven. However, since new institutions such as non-government organisations and private sector are taking important roles in the actual delivery of services, there is a greater need to understand the existing linkages to promote integration. The informal sector also provides services to a large number of population. In order to promote efficient and effective delivery of urban services, it is important to work on the integrated approaches involving key stakeholders.     

More than just nickels and dimes: a cross-national analysis of working poverty in affluent democracies

Despite its centrality to contemporary inequality, working poverty is often popularly discussed but rarely studied by sociologists. Using the Luxembourg Income Study (2009), we analyze whether an individual is working poor across 18 affluent democracies circa 2000. We demonstrate that working poverty does not simply mirror overall poverty and that there is greater cross-national variation in working than overall poverty. We then examine four explanations for working poverty: demographic characteristics, economic performance, unified theory, and welfare generosity. We utilize Heckman probit models to jointly model the likelihood of employment and poverty among the employed. Our analyses provide the least support for the economic performance explanation. There is modest support for unified theory as unionization reduces working poverty in some models. However, most of these effects appear to be mediated by welfare generosity. More substantial evidence exists for the demographic characteristics and welfare generosity explanations. An individual's likelihood of being working poor can be explained by (a) a lack of multiple earners or other adults in one's household, low education, single motherhood, having children and youth; and (b) the generosity of the welfare state in which he or she resides. Also, welfare generosity does not undermine employment and reduces working poverty even among demographically vulnerable groups. Ultimately, we encourage a greater role for the welfare state in debates about working poverty.