20 research outputs found

    Testing predictive performance of binary choice models

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    Binary choice models occur frequently in economic modeling. A measure of the predictive performance of binary choice models that is often reported is the hit rate of a model. This paper develops a test for the outperformance of a predictor for binary outcomes over a naive prediction method, which predicts the outcome that is most often observed. This is done for a general class of prediction models, including the well known Probit and Logit models. In many cases the test is easy to compute. The test is then applied and compared to a general test of Pesaran and Timmermann (1992) for dependence between predictors and realizations

    The institutional choice of refuse collection: Determining variables in the Netherlands

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    General empirical evidence suggests that contracting out refuse collection results in a cost decrease in the order of 20%. However, although the method of contracting out refuse collection has become more popular, it is still less common than in-house provision. This paper investigates the reasons behind this phenomenon. Recently, L?pez-de-Silanes et al. (1997) tried to explain the reservedness of local authorities towards contracting out with US-data and show that political patronage is an important explanation. In this article we give such an empirical assessment using Dutch data. We base our empirical research on the combination of the theories around efficiency, interest group influence and ideology. To test these theories we model the choice between private and public provision of refuse collection on the one hand side and the choice between in-house and out-house provision on the other side. Data are available for nearly all Dutch municipalities. A most striking conclusion is that nearly all political parties in the Netherlands do have a preference for public and in-house provision of refuse collection. We find only modest evidence for the hypothesis that a high level of real estate tax (proxy for the soundness of municipal financial affairs, the efficiency argument) or a low leve

    Future trends of life expectancy by education in the Netherlands

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    Background National projections of life expectancy are made periodically by statistical offices or actuarial societies in Europe and are widely used, amongst others for reforms of pension systems. However, these projections may not provide a good estimate of the future trends in life expectancy of different social-economic groups. The objective of this study is to provide insight in future trends in life expectancies for low, mid and high educated men and women living in the Netherlands. Methods We used a three-layer Li and Lee model with data from neighboring countries to complement Dutch time series. Results Our results point at further increases of life expectancy between age 35 and 85 and of remaining life expectancy at age 35 and age 65, for all education groups in the Netherlands. The projected increase in life expectancy is slightly larger among the high educated than among the low educated. Life expectancy of low educated women, particularly between age 35 and 85, shows the smallest projected increase. Our results also suggest that inequalities in life expectancies between high and low educated will be similar or slightly increasing between 2018 and 2048. We see no indication of a decline in inequality between the life expectancy of the low and high educated. Conclusions The educational inequalities in life expectancy are expected to persist or slightly increase for both men and women. The persistence and possible increase of inequalities in life expectancy between the educational groups may cause equity concerns of increases in pension age that are equal among all socio-economic groups

    Projecting years in good health between age 50-69 by education in the Netherlands until 2030 using several health indicators-an application in the context of a changing pension age

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    Objective: We investigate whether there are changes over time in years in good health people can expect to live above (surplus) or below (deficit) the pension age, by level of attained education, for the past (2006), present (2018) and future (2030) in the Netherlands. Methods: We used regression analysis to estimate linear trends in prevalence of four health indicators: self-assessed health (SAH), the Organization for Economic Co-operation and Development (OECD) functional limitation indicator, the OECD indicator without hearing and seeing, and the activities-of-daily-living (ADL) disability indicator, for individuals between 50 and 69 years of age, by age category, gender and education using the Dutch National Health Survey (1989–2018). We combined these prevalence estimates with past and projected mortality data to obtain estimates of years lived in good health. We calculated how many years individuals are expected to live in good health above (surplus) or below (deficit) the pension age for the three points in time. The pension ages used were 65 years for 2006, 66 years for 2018 and 67.25 years for 2030. Results: Both for low educated men and women, our analyses show an increasing deficit of years in good health relative to the pension age for most outcomes, particularly for the SAH and OECD indicator. For high educated we find a decreasing surplus of years lived in good health for all indicators with the exception of SAH. For women, absolute inequalities in the deficit or surplus of years in good health between low and high educated appear to be increasing over time. Conclusions: Socio-economic inequalities in trends of mortality and the prevalence of ill-health, combined with increasing statutory pension age, impact the low educated more adversely than the high educated. Policies are needed to mitigate the increasing deficit of years in good health relative to the pension age, particularly among the low educated

    Mean-Coherent Risk and Mean-Variance Approaches in Portfolio Selection: An Empirical Comparison

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    We empirically analyze the implementation of coherent risk measures in portfolio selection.First, we compare optimal portfolios obtained through mean-coherent risk optimization with corresponding mean-variance portfolios.We find that, even for a typical portfolio of equities, the outcomes can be statistically and economically different.Furthermore, we apply spanning tests for the mean-coherent risk efficient frontiers, which we compare to their equivalents in the meanvariance framework.For portfolios of common stocks the outcomes of the spanning tests seem to be statistically the same.

    Environmental Kuznets Curves for CO2: Heterogeneity versus Homogeneity

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    We explore the emissions income relationship for CO2 in OECD countries using various modelling strategies.Even for this relatively homogeneous sample, we find that the inverted-U-shaped curve is quite sensitive to the degree of heterogeneity included in the panel estimations.This finding is robust, not only across different model specifications but also across estimation techniques, including the more flexible non-parametric approach.Differences in restrictions applied in panel estimations are therefore responsible for the widely divergent findings for an inverted-U shape for CO2.Our findings suggest that allowing for enough heterogeneity is essential to prevent spurious correlation from reduced-form panel estimations.Moreover, this inverted U for CO2 is likely to exist for many, but not for all, countries.

    The Non- and Semiparametric Analysis of MS Models: Some Applications

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    This paper illustrates how to compare different microscopic simulation (MS) models and how to compare a MS model with real data in case the parameters of interest are estimated non- or semiparametrically.As examples we investigate the marginal single-period probability density function of stock returns, and the corresponding spectral density function and memory parameters.We illustrate the methodology by the MS models developed by Levy, Levy, Solomon (2000) and the market fraction model developed by He and Li (2005a, b), and confront the resulting return data with the S&P 500 stock index data.
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