The Relative Contribution of Health Status and Quality of Life Domains in Subjective Health in Old Age

To analyze the influence of different health status dimensions and quality of life (QoL) domains on older adults’ subjective health, and to assess the role that residential satisfaction plays in these relationships. A QoL survey was conducted on a representative sample of the community-dwelling older adult population in Madrid province (Spain). Logistic regression models were applied to studying: the health status dimensions associated with satisfaction with health; the relationship between satisfaction with health and other QoL domains; and, the influence of these domains on satisfaction with life. Sociodemographic and residential characteristics were included in all the models. The determinants of satisfaction with health in the first model were: mobility, usual activities, morbidity, and satisfaction with neighborhood. QoL domains associated with health were: leisure activities, neighborhood, and finances. Satisfaction with life was explained by these three domains, along with age, family and health. In sum, leisure, neighborhood, and finances showed a positive effect on satisfaction with health and with life.     

On CUSUM test for dynamic panel models

Statistical Methods & Applications - In this study, we consider the problem of testing for a parameter change in dynamic panel models with fixed effects. As a test, we suggest using the CUSUM...     

Reluctant entrepreneurs: musicians and entrepreneurship in the ‘new’ music industry

Changing labour conditions in the creative industries – with celebrations of autonomy and entrepreneurialism intertwined with increasing job insecurity, portfolio careers and short‐term, project‐based contracts – are often interpreted as heralding changes to employment relations more broadly. The position of musicians’ labour in relation to these changes is unclear, however, given that these kinds of conditions have defined musicians’ working practices over much longer periods of time (though they may have intensified due to well‐documented changes to the music industry brought about by digitization and disintermediation). Musicians may thus be something of a barometer of current trends, as implied in the way that the musically derived label ‘gig economy’ is being used to describe the spread of precarious working conditions to broader sections of the population. This article, drawing on original qualitative research that investigated the working practices of musicians, explores one specific aspect of these conditions: whether musicians are self‐consciously entrepreneurial towards their work and audience. We found that, while the musicians in our study are routinely involved in activities that could be construed as entrepreneurial, generally they were reluctant to label themselves as entrepreneurs. In part this reflected understandings of entrepreneurialism as driven by profit‐seeking but it also reflected awareness that being a popular musician has always involved business and commercial dimensions. Drawing on theoretical conceptions of entrepreneurship developed by Joseph Schumpeter we highlight how the figure of the entrepreneur and the artist/musician share much in common and reflect various aspects of romantic individualism. Despite this, there are also some notable differences and we conclude that framing musicians’ labour as entrepreneurial misrepresents their activities through an overemphasis on the economic dimensions of their work at the expense of the cultural.     

Macroeconomic Uncertainty Through the Lens of Professional Forecasters

ABSTRACT

We analyze the evolution of macroeconomic uncertainty in the United States, based on the forecast errors of consensus survey forecasts of various economic indicators. Comprehensive information contained in the survey forecasts enables us to capture a real-time measure of uncertainty surrounding subjective forecasts in a simple framework. We jointly model and estimate macroeconomic (common) and indicator-specific uncertainties of four indicators, using a factor stochastic volatility model. Our macroeconomic uncertainty estimates have three major spikes has three major spikes aligned with the 1973–1975, 1980, and 2007–2009 recessions, while other recessions were characterized by increases in indicator-specific uncertainties. We also show that the selection of data vintages affects the estimates and relative size of jumps in estimated uncertainty series. Finally, our macroeconomic uncertainty has a persistent negative impact on real economic activity, rather than producing “wait-and-see” dynamics.     

Finite sample performance of frequency- and time-domain tests for seasonal fractional integration

Testing the order of integration of economic and financial time series has become a conventional procedure prior to any modelling exercise. In this paper, we investigate and compare the finite sample properties of the frequency-domain tests proposed by Robinson [Efficient tests of nonstationary hypotheses, J. Amer. Statist. Assoc. 89(428) (1994), pp. 1420–1437] and the time-domain procedure proposed by Hassler, Rodrigues, and Rubia [Testing for general fractional integration in the time domain, Econometric Theory 25 (2009), pp. 1793–1828] when applied to seasonal data. The results presented are of empirical relevance as they provide some guidance regarding the finite sample properties of these tests.     

Is It Always Necessary to Take Sample Selection into Account?

We compare a simple ordinary least squares (OLS) with the maximum likelihood estimation of the Tobit I and Tobit II regression models, in the selected sample. We propose a new measure to quantify the performance of OLS.     

Measuring Time Series Predictability Using Support Vector Regression

Most studies involving statistical time series analysis rely on assumptions of linearity, which by its simplicity facilitates parameter interpretation and estimation. However, the linearity assumption may be too restrictive for many practical applications. The implementation of nonlinear models in time series analysis involves the estimation of a large set of parameters, frequently leading to overfitting problems. In this article, a predictability coefficient is estimated using a combination of nonlinear autoregressive models and the use of support vector regression in this model is explored. We illustrate the usefulness and interpretability of results by using electroencephalographic records of an epileptic patient.     

Use of the Bootstrap and Cross-Validation in Ridge Regression

Several existing methods for the choice of the ridge parameter are reviewed, and a bootstrap method is proposed. The bootstrap provides independent measures of prediction errors based on multiple predictions along with an estimate of the standard error of prediction. The bootstrap and selected competitors are compared through Monte Carlo simulations for various degrees of design matrix collinearity and varying levels of signal-to-noise ratio. The procedure is also illustrated by application to two published data sets. In one case, the bootstrap choice of the ridge parameter leads to a smaller mean squared error of prediction than the ridge trace method. In the second case, an optimal choice of no perturbation is confirmed. Benefits of the bootstrap choice include its less subjective nature, ease of implementation, and robustness.