Parametric and semiparametric estimation of sample selection models: an empirical application to the female labour force in Portugal

This comment corrects the errors in the estimation process that appear in Martins (2001). The first error is in the parametric probit estimation, as the previously presented results do not maximize the log-likelihood function. In the global maximum more variables become significant. As for the semiparametric estimation method, the kernel function used in Martins (2001) can take on both positive and negative values, which implies that the participation probability estimates may be outside the interval [0,1]. We have solved the problem by applying local smoothing in the kernel estimation, as suggested by Klein and Spady (1993).

Comparison of parametric and nonparametric methods to map oligogenes by linkage

A sample of 95 sib pairs affected with insulin-dependent diabetes and typed with their normal parents for 28 markers on chromosome 6 has been analyzed by several methods. When appropriate parameters are efficiently estimated, a parametric model is equivalent to the β model, which is superior to nonparametric alternatives both in single point tests (as found previously) and in multipoint tests. Theory is given for meta-analysis combined with allelic association, and problems that may be associated with errors of map location and/or marker typing are identified. Reducing by multipoint analysis the number of association tests in a dense map can give a 3-fold reduction in the critical lod, and therefore in the cost of positional cloning.

Empirical evidence on real convergence across brazilian states

This paper examines the real convergence hypothesis across Brazilian states. In order to test for the existence of income convergence the or- der of integration of real Gross State Product (GSP) per capita series is examined as well as their di¤erences with respect to the São Paulo state which is used as a benchmark state. Both parametric and semiparametric methods are used and the results show that convergence is achieved in the cases of Alagoas, Amazonas, Bahia, Goiás, Mato Grosso, Minas Gerais, Pernambuco, Piauí, Rio Grande do Sul, Rio de Janeiro and Santa Cata- rina and convergence is weakly achieved in the cases of Ceará, Maranhao, Pará, Paraná and Sergipe .The states of Espírito Santo, Paraíba and Rio Grande do Norte show no convergence. O artigo examina a hipótese de convergência real entre os estados brasileiros. Para testar a existência ou não da convergência da renda a ordem da integração da série do produto real bruto do estado per capita é examinada assim como suas diferenças com respeito ao estado de São Paulo que é usado como base. Foram utilizados métodos paramétricos e semiparametric e os resultados mostram que ocorre convergência nos estados: Alagoas, Amazonas, Baía, Goiás, Mato Grosso, Minas Gerais, Pernambuco, Piauí, Rio Grande do Sul, Rio de Janeiro e Santa Catarina e ocorre convergência fraca nos estados: Ceará, de Maranhão, Pará, Paraná e Sergipe. Nos estado

Valor en Riesgo: Evaluación del desempeño de diferentes metodologías para 7 países latinoamericanos

Este documento evalúa el comportamiento de diferentes métodos (paramétrico, no paramétricos y semi-paramétricos) para estimar el VaR (valor en riesgo) de un portafolio representativo para 7 países latinoamericanos. El cálculo del VaR implica la estimación del i-esimo percentil de la distribución del valor futuro del valor de un portafolio. Los resultados no muestran la existencia de un método que se comporte mejor que los demás. Con un nivel de confianza del 95% los modelos paramétricos que emplean el EWMA se desempeñan en general bien así como con el TGARCH, pero estos modelos tienen un comportamiento pobre cuando la significancia considerada es del 1%.

Parametric and Nonparametric Approaches to Price and Tax Reform.

In the analysis of tax reform, when equity is traded off against efficiency, the measurement of the latter requires us to know how tax-induced price changes affect quantities supplied and demanded. in this paper, we present various econometric procedures for estimating how taxes affect demand.

Semiparametric Methods for Semi-competing Risks Problem with Censoring and Truncation

Studies of chronic life-threatening diseases often involve both mortality and morbidity. In observational studies, the data may also be subject to administrative left truncation and right censoring. Since mortality and morbidity may be correlated and mortality may censor morbidity, the Lynden-Bell estimator for left truncated and right censored data may be biased for estimating the marginal survival function of the non-terminal event. We propose a semiparametric estimator for this survival function based on a joint model for the two time-to-event variables, which utilizes the gamma frailty specification in the region of the observable data. Firstly, we develop a novel estimator for the gamma frailty parameter under left truncation. Using this estimator, we then derive a closed form estimator for the marginal distribution of the non-terminal event. The large sample properties of the estimators are established via asymptotic theory. The methodology performs well with moderate sample sizes, both in simulations and in an analysis of data from a diabetes registry.

Has DRG payment influenced the technical efficiency and productivity of diagnostic technologies in Portuguese public hospitals? An empirical analysis using parametric and non‐parametric methods

The use of Diagnosis Related Groups (DRG) as a mechanism for hospital financing is a currently debated topic in Portugal. The DRG system was scheduled to be initiated by the Health Ministry of Portugal on January 1, 1990 as an instrument for the allocation of public hospital budgets funded by the National Health Service (NHS), and as a method of payment for other third party payers (e.g., Public Employees (ADSE), private insurers, etc.). Based on experience from other countries such as the United States, it was expected that implementation of this system would result in more efficient hospital resource utilisation and a more equitable distribution of hospital budgets. However, in order to minimise the potentially adverse financial impact on hospitals, the Portuguese Health Ministry decided to gradually phase in the use of the DRG system for budget allocation by using blended hospitalspecific and national DRG casemix rates. Since implementation in 1990, the percentage of each hospitals budget based on hospital specific costs was to decrease, while the percentage based on DRG casemix was to increase. This was scheduled to continue until 1995 when the plan called for allocating yearly budgets on a 50% national and 50% hospitalspecific cost basis. While all other nonNHS third party payers are currently paying based on DRGs, the adoption of DRG casemix as a National Health Service budget setting tool has been slower than anticipated. There is now some argument in both the political and academic communities as to the appropriateness of DRGs as a budget setting criterion as well as to their impact on hospital efficiency in Portugal. This paper uses a twostage procedure to assess the impact of actual DRG payment on the productivity (through its components, i.e., technological change and technical efficiency change) of diagnostic technology in Portuguese hospitals during the years 1992–1994, using both parametric and nonparametric frontier models. We find evidence that the DRG payment system does appear to have had a positive impact on productivity and technical efficiency of some commonly employed diagnostic technologies in Portugal during this time span.

SEMIPARAMETRIC METHODS IN THE ESTIMATION OF TAIL PROBABILITIES AND EXTREME QUANTILES

In quantitative risk analysis, the problem of estimating small threshold exceedance probabilities and extreme quantiles arise ubiquitously in bio-surveillance, economics, natural disaster insurance actuary, quality control schemes, etc. A useful way to make an assessment of extreme events is to estimate the probabilities of exceeding large threshold values and extreme quantiles judged by interested authorities. Such information regarding extremes serves as essential guidance to interested authorities in decision making processes. However, in such a context, data are usually skewed in nature, and the rarity of exceedance of large threshold implies large fluctuations in the distribution's upper tail, precisely where the accuracy is desired mostly. Extreme Value Theory (EVT) is a branch of statistics that characterizes the behavior of upper or lower tails of probability distributions. However, existing methods in EVT for the estimation of small threshold exceedance probabilities and extreme quantiles often lead to poor predictive performance in cases where the underlying sample is not large enough or does not contain values in the distribution's tail. In this dissertation, we shall be concerned with an out of sample semiparametric (SP) method for the estimation of small threshold probabilities and extreme quantiles. The proposed SP method for interval estimation calls for the fusion or integration of a given data sample with external computer generated independent samples. Since more data are used, real as well as artificial, under certain conditions the method produces relatively short yet reliable confidence intervals for small exceedance probabilities and extreme quantiles.

Parametric and nonparametric statistical modelling of crop yield: implications for pricing crop insurance contracts

This article considers alternative methods to calculate the fair premium rate of crop insurance contracts based on county yields. The premium rate was calculated using parametric and nonparametric approaches to estimate the conditional agricultural yield density. These methods were applied to a data set of county yield provided by the Statistical and Geography Brazilian Institute (IBGE), for the period of 1990 through 2002, for soybean, corn and wheat, in the State of Paran. In this article, we propose methodological alternatives to pricing crop insurance contracts resulting in more accurate premium rates in a situation of limited data.

Upper Arm Measurements of Healthy Neonates Comparing Ultrasonography and Anthropometric Methods

Objective: To compare measurements of the upper arm cross-sectional areas (total arm area,arm muscle area, and arm fat area of healthy neonates) as calculated using anthropometry with the values obtained by ultrasonography. Materials and methods: This study was performed on 60 consecutively born healthy neonates: gestational age (mean6SD) 39.661.2 weeks, birth weight 3287.16307.7 g, 27 males (45%) and 33 females (55%). Mid-arm circumference and tricipital skinfold thickness measurements were taken on the left upper mid-arm according to the conventional anthropometric method to calculate total arm area, arm muscle area and arm fat area. The ultrasound evaluation was performed at the same arm location using a Toshiba sonolayer SSA-250AÒ, which allows the calculation of the total arm area, arm muscle area and arm fat area by the number of pixels enclosed in the plotted areas. Statistical analysis: whenever appropriate, parametric and non-parametric tests were used in order to compare measurements of paired samples and of groups of samples. Results: No significant differences between males and females were found in any evaluated measurements, estimated either by anthropometry or by ultrasound. Also the median of total arm area did not differ significantly with either method (P50.337). Although there is evidence of concordance of the total arm area measurements (r50.68, 95% CI: 0.55–0.77) the two methods of measurement differed for arm muscle area and arm fat area. The estimated median of measurements by ultrasound for arm muscle area were significantly lower than those estimated by the anthropometric method, which differed by as much as 111% (P,0.001). The estimated median ultrasound measurement of the arm fat was higher than the anthropometric arm fat area by as much as 31% (P,0.001). Conclusion: Compared with ultrasound measurements using skinfold measurements and mid-arm circumference without further correction may lead to overestimation of the cross-sectional area of muscle and underestimation of the cross-sectional fat area. The correlation between the two methods could be interpreted as an indication for further search of correction factors in the equations.

An Introduction to parametric and non-parametric models for bivariate positive insurance claim severity distributions

We present a real data set of claims amounts where costs related to damage are recorded separately from those related to medical expenses. Only claims with positive costs are considered here. Two approaches to density estimation are presented: a classical parametric and a semi-parametric method, based on transformation kernel density estimation. We explore the data set with standard univariate methods. We also propose ways to select the bandwidth and transformation parameters in the univariate case based on Bayesian methods. We indicate how to compare the results of alternative methods both looking at the shape of the overall density domain and exploring the density estimates in the right tail.

Estimation of parametric and nonparametric models for univariate claim severity distributions - an approach using R

This paper presents an analysis of motor vehicle insurance claims relating to vehicle damage and to associated medical expenses. We use univariate severity distributions estimated with parametric and non-parametric methods. The methods are implemented using the statistical package R. Parametric analysis is limited to estimation of normal and lognormal distributions for each of the two claim types. The nonparametric analysis presented involves kernel density estimation. We illustrate the benefits of applying transformations to data prior to employing kernel based methods. We use a log-transformation and an optimal transformation amongst a class of transformations that produces symmetry in the data. The central aim of this paper is to provide educators with material that can be used in the classroom to teach statistical estimation methods, goodness of fit analysis and importantly statistical computing in the context of insurance and risk management. To this end, we have included in the Appendix of this paper all the R code that has been used in the analysis so that readers, both students and educators, can fully explore the techniques described

A limited area model intercomparison on the "Montserrat-2000" flash-flood event using statistical and deterministic methods

In the scope of the European project Hydroptimet, INTERREG IIIB-MEDOCC programme, limited area model (LAM) intercomparison of intense events that produced many damages to people and territory is performed. As the comparison is limited to single case studies, the work is not meant to provide a measure of the different models' skill, but to identify the key model factors useful to give a good forecast on such a kind of meteorological phenomena. This work focuses on the Spanish flash-flood event, also known as "Montserrat-2000" event. The study is performed using forecast data from seven operational LAMs, placed at partners' disposal via the Hydroptimet ftp site, and observed data from Catalonia rain gauge network. To improve the event analysis, satellite rainfall estimates have been also considered. For statistical evaluation of quantitative precipitation forecasts (QPFs), several non-parametric skill scores based on contingency tables have been used. Furthermore, for each model run it has been possible to identify Catalonia regions affected by misses and false alarms using contingency table elements. Moreover, the standard "eyeball" analysis of forecast and observed precipitation fields has been supported by the use of a state-of-the-art diagnostic method, the contiguous rain area (CRA) analysis. This method allows to quantify the spatial shift forecast error and to identify the error sources that affected each model forecasts. High-resolution modelling and domain size seem to have a key role for providing a skillful forecast. Further work is needed to support this statement, including verification using a wider observational data set.

Improved combinations of parametric and nonparametric Regression estimators

This paper provides a systematic and unified treatment of the developments in the area of kernel estimation in econometrics and statistics. Both the estimation and hypothesis testing issues are discussed for the nonparametric and semiparametric regression models. A discussion on the choice of windowwidth is also presented.