Semiparametric Bootstrap Research Articles

Contextual factors and efficiency gains in Italian tourist destinations

The paper investigates the efficiency of 103 Italian provinces and some contextual factors that influence the competitiveness of a territorial tourist destination, defined as an ensemble of different historical, economic, environmental, and social traditions. Non-parametric Data Envelopment Analysis and semi-parametric bootstrapping extensions are applied over the period 2012-2019. The findings demonstrate that tourism destinations are considerably technically and scale inefficient, and that the total factor productivity is decreasing over time. The results also present a great heterogeneity of Italian provinces regarding their efficiency levels, suggesting that the traditional North-South regional divide of Italy does not apply in the tourism sector. Regarding the role of contextual factors as sources of efficiency gains, the cultural attractions, cultural public expenditures, and the rule of law increase technical efficiency of Italian provinces, whilst provinces with higher income and higher pollution receive negative gains.

A Semiparametric Approach to Test for the Presence of INAR: Simulations and Empirical Applications

The present paper explores the application of bootstrap methods in testing for serial dependence in observed driven Integer-AutoRegressive (models) considering Poisson arrivals (P-INAR). To this end, a new semiparametric and restricted bootstrap algorithm is developed to ameliorate the performance of the score-based test statistic, especially when the time series present small or moderately small lengths. The performance of the proposed bootstrap test, in terms of empirical size and power, is investigated through a simulation study even considering deviation from Poisson assumptions for innovations, i.e., overdispersion and underdispersion. Under non-Poisson innovations, the semiparametric bootstrap seems to “restore” inference, while the asymptotic test usually fails. Finally, the usefulness of this approach is shown via three empirical applications.

Replicated Spatial Point Pattern Analyses for Ecological Inference: A Tutorial Using the RSPPlme4 Package in R

The analysis of spatial point patterns has greatly advanced our understanding of ecological processes. However, the methods currently available for analyzing replicated spatial point patterns (RSPPs) are rarely used by ecologists. One barrier to the use of RSPP analyses is a lack of software to implement the approaches that have been developed in the statistical literature. Here, we provide a practical guide to RSPP analysis and introduce the RSPPlme4 R package that implements the approaches we discuss. The methods we outline use a linear modeling framework to link variation in the spatial structure of point patterns to discrete and continuous explanatory covariates. We describe methods for linear models and mixed-effects models of RSPPs, including approaches to estimating confidence intervals via semi-parametric bootstrapping. The syntax for model fitting is similar to that used in linear and linear mixed-effects modeling packages in R. The RSPPlme4 package also allows users to easily plot the results of model fits. We hope that this tutorial will make methods for RSPP analysis accessible to a wide range of ecologists and open new avenues for gaining insight into ecological processes from spatial data.

On the performance of cryptocurrency funds

We investigate the performance of funds that specialise in cryptocurrency markets. In doing so, we contribute to a growing literature that aims to understand the value of digital assets as investments. The main empirical results support the argument that cryptocurrency funds generate significantly positive alphas compared to passive benchmarks or conventional risk factors. To understand whether the fund managers have sufficient skills to more than cover their costs, we compare the actual fund alphas against the simulated values from a panel semi-parametric bootstrap approach. The analysis shows that the extreme outperformance is unlikely to be explained by the luck of fund managers. However, the significance of the alphas becomes statistically weaker after considering the cross-sectional correlation in fund returns.

The asymptotic distribution of robust maximum likelihood estimator with Huber function for the mixed spatial autoregressive model with outliers

There is a wide range of outliers in spatial data, and these potential outliers will have a great impact on parameter estimation and corresponding statistical inference. Relying on the framework of maximum likelihood estimation (MLE), we investigate the asymptotic distribution of robust ML estimator under the mixed spatial autoregressive models with outliers and compare it with that of the ML estimator. Furthermore, based on the asymptotic theoretical result, we conduct the confidence interval of robust MLE and MLE. Similar to the results of MLE, we construct the second-order-corrected robust confidence interval using the parametric and semi-parametric bootstrap method. Simulation studies using Monte Carlo show that the robust estimator with the Huber loss function is more accurate and outperforms the MLE in most sample settings when data is contaminated by outliers. Then the use of the method is demonstrated in the analysis of the Neighborhood Crimes Data and the Boston Housing Price Data. The results further support the eligibility of the robust method in practical situations.

Parametric and semi-parametric bootstrap-based confidence intervals for robust linear mixed models

The linear mixed model (LMM) is a popular statistical model for the analysis of longitudinal data. However, the robust estimation of and inferential conclusions for the LMM in the presence of outliers (i.e., observations with very low probability of occurrence under Normality) is not part of mainstream longitudinal data analysis. In this work, we compared the coverage rates of confidence intervals (CIs) based on two bootstrap methods, applied to three robust estimation methods. We carried out a simulation experiment to compare CIs under three different conditions: data 1) without contamination, 2) contaminated by within-, or 3) between-participant outliers. Results showed that the semi-parametric bootstrap associated to the composite tau-estimator leads to valid inferential decisions with both uncontaminated and contaminated data. This being the most comprehensive study of CIs applied to robust estimators of the LMM, we provide fully commented R code for all methods applied to a popular example.

Integration in banking efficiency: a comparative analysis of the European Union, the Eurozone, and the United States banks

PurposeThis paper investigates the development of efficiency and the progress of banking integration in the European Union by checking for convergence among banks of European and Eurozone countries as well as contrasting the results with those of United States banks.Design/methodology/approachInitially, we employ the two-stage semi-parametric double bootstrap DEA method, which absorbs the effects of possible integration barriers in the measurement of efficiency. Afterwards, we apply a panel data model, in order to investigate the process of banking integration by testing for convergence and for convergent clusters in banking efficiency.FindingsOur main findings show that the bank efficiency of the US is considerably higher than that of the Eurozone and the European Union. Although there is no evidence of convergence across the banking groups, our results indicate the presence of club convergence. We also conclude that the US banking system is closer to convergence than the Eurozone and the European Union banks. Nevertheless, this outcome is subject to change in the future due to the fact that Eurozone and European Union banks' speed of convergence is higher than that of US banks.Originality/valueOur survey is unique in trying to check for convergence while controlling for country-specific and bank-specific factors that affect the efficiency of European and Eurozone banks. Moreover, recent literature does not compare the convergence of efficiency of Eurozone, European and US banking. Finally, in our paper special consideration was given to the comparison of commercial, cooperative and savings banks, as subsets of our banking groups.

Population size estimation based upon zero-truncated, one-inflated and sparse count data

Estimating the size of a hard-to-count population is a challenging matter. In particular, when only few observations of the population to be estimated are available. The matter gets even more complex when one-inflation occurs. This situation is illustrated with the help of two examples: the size of a dice snake population in Graz (Austria) and the number of flare stars in the Pleiades. The paper discusses how one-inflation can be easily handled in likelihood approaches and also discusses how variances and confidence intervals can be obtained by means of a semi-parametric bootstrap. A Bayesian approach is mentioned as well and all approaches result in similar estimates of the hidden size of the population. Finally, a simulation study is provided which shows that the unconditional likelihood approach as well as the Bayesian approach using Jeffreys’ prior perform favorable.

Premium estimation in the fire insurance through semiparametric bootstrap

Along with the development of information, science and technology, there is a quite popular developing resampling method, namely bootstrapping. Bootstrap estimates asymptotically against its original value (observation). Thus, the greater bootstrap replication, the resample distribution will be normally distributed. It indicates that the bootstrap estimator gives better results. Based on the goodness-of-fit test by using Kolmogorov-Smirnov test, the severity on fire insurance data follow Weibull 2 parameter distribution. A case study is conducted on reinsurance company’s data for shopping centre’s fire. It is a big data. Since it is a reinsurance company’s data, the data completeness may be inadequate. It causes the severity claim to be processed using semiparametric bootstrap.

Comparing methods to estimate the proportion of turbine-induced bird and bat mortality in the search area under a road and pad search protocol

Estimating bird and bat mortality at wind facilities typically involves searching for carcasses on the ground near turbines. Some fraction of carcasses inevitably lie outside the search plots, and accurate mortality estimation requires accounting for those carcasses using models to extrapolate from searched to unsearched areas. Such models should account for variation in carcass density with distance, and ideally also for variation with direction (anisotropy). We compare five methods of accounting for carcasses that land outside the searched area (ratio, weighted distribution, non-parametric, and two generalized linear models (glm)) by simulating spatial arrival patterns and the detection process to mimic observations which result from surveying only, or primarily, roads and pads (R&P) and applying the five methods. Simulations vary R&P configurations, spatial carcass distributions (isotropic and anisotropic), and per turbine fatality rates. Our results suggest that the ratio method is less accurate with higher variation relative to the other four methods which all perform similarly under isotropy. All methods were biased under anisotropy; however, including direction covariates in the glm method substantially reduced bias. In addition to comparing methods of accounting for unsearched areas, we suggest a semiparametric bootstrap to produce confidence-based bounds for the proportion of carcasses that land in the searched area.

On the Performance of Cryptocurrency Funds

We investigate the performance of funds that specialise in cryptocurrency markets. In doing so, we contribute to a growing literature that aims to understand the value of digital assets as investments. The main empirical results provide support to the idea that cryptocurrency funds generate significantly positive alphas compared to passive benchmarks or conventional risk factors. To understand whether the outperformance arises from the skills or luck of fund managers, we compare the actual fund alphas against the simulated values from a panel semi-parametric bootstrap approach. The analysis shows that the extreme outperformance is unlikely to be explained by the luck of fund managers. However, the significance of the alphas becomes statistically weaker after considering the cross-sectional correlation in fund returns.

Semiparametric Bootstrapping Forestimating Parameters in Krigingmodel for Deterministic Simulations

In practice, the parameters in the Kriging model are unknown, but they can be estimated based on the behaviour of the observed data. Parameters in the Kriging model can be estimated based on the consideration of Regression-Kriging models. Regression-Kriging models are Universal Kriging models with polynomials of degree zero (Ordinary Kriging), one (Universal Kriging with degree one), or two (Universal Kriging with degree two). A new method for estimating the parameters in the Kriging model with semiparametric bootstrapping is proposed in this paper. The semiparametric bootstrapping procedure works by combining the bootstrap method and Kriging.

Data-Driven Transformations in Small Area Estimation

SummarySmall area models typically depend on the validity of model assumptions. For example, a commonly used version of the empirical best predictor relies on the Gaussian assumptions of the error terms of the linear mixed regression model: a feature rarely observed in applications with real data. The paper tackles the potential lack of validity of the model assumptions by using data-driven scaled transformations as opposed to ad hoc chosen transformations. Different types of transformations are explored, the estimation of the transformation parameters is studied in detail under the linear mixed regression model and transformations are used in small area prediction of linear and non-linear parameters. The use of scaled transformations is crucial as it enables fitting the linear mixed regression model with standard software and hence it simplifies the work of the data analyst. Mean-squared error estimation that accounts for the uncertainty due to the estimation of the transformation parameters is explored by using the parametric and semiparametric (wild) bootstrap. The methods proposed are illustrated by using real survey and census data for estimating income deprivation parameters for municipalities in the Mexican state of Guerrero. Simulation studies and the results from the application show that using carefully selected, data-driven transformations can improve small area estimation.

Goodness of governance effect on European banking efficiency

The main aim of this paper is to determine the limitations on the scope for the improvement of banking efficiency caused by European goodness of government specifically in the post-crisis period. A semiparametric bootstrap approach leads to the estimation of bias-corrected efficiencies and to their regression on goodness of governance dimensions and indicators. Estimations show that goodness of government, structured into selection process, formulation and implementation of policies and stakeholders’ respect, exerts a positive and significant influence on European banking efficiency. In this line, an innovative measure for the goodness of governance effect on efficiency is suggested by comparing metafrontier and specific frontiers estimates. Deviations determine a limitation on the scope for the improvement of banking efficiency, which is specifically relevant for countries with low goodness of government.

Inference for L-estimators of location using a bootstrap warping approach

In this note we propose a new semi-parametric bootstrap procedure for hypothesis tests about a statistical function and termed bootstrap warping. This procedure was motivated by empirical likelihood and bootstrap tilting techniques. The procedure is computationally efficient and has a fixed number of parameters. We show that the warping procedure has good type I error control and has monotone power as a function of sample size and shift alternatives.

Tobit regression for modeling mean survival time using data subject to multiple sources of censoring.

Mean survival time is often of inherent interest in medical and epidemiologic studies. In the presence of censoring and when covariate effects are of interest, Cox regression is the strong default, but mostly due to convenience and familiarity. When survival times are uncensored, covariate effects can be estimated as differences in mean survival through linear regression. Tobit regression can validly be performed through maximum likelihood when the censoring times are fixed (ie, known for each subject, even in cases where the outcome is observed). However, Tobit regression is generally inapplicable when the response is subject to random right censoring. We propose Tobit regression methods based on weighted maximum likelihood which are applicable to survival times subject to both fixed and random censoring times. Under the proposed approach, known right censoring is handled naturally through the Tobit model, with inverse probability of censoring weighting used to overcome random censoring. Essentially, the re-weighting data are intended to represent those that would have been observed in the absence of random censoring. We develop methods for estimating the Tobit regression parameter, then the population mean survival time. A closed form large-sample variance estimator is proposed for the regression parameter estimator, with a semiparametric bootstrap standard error estimator derived for the population mean. The proposed methods are easily implementable using standard software. Finite-sample properties are assessed through simulation. The methods are applied to a large cohort of patients wait-listed for kidney transplantation.

NATURAL HEDGING IN LONG-TERM CARE INSURANCE

AbstractWe investigate the application of natural hedging strategies for long-term care (LTC) insurers by diversifying both longevity and disability risks affecting LTC annuities. We propose two approaches to natural hedging: one built on a multivariate duration, the other on the Conditional Value-at-Risk minimization of the unexpected loss. Both the approaches are extended to the LTC insurance using a multiple state framework. In order to represent the future evolution of mortality and disability transition probabilities, we use the stochastic model of Cairns et al. (2009) with cohort effect under parameter uncertainty through a semi-parametric bootstrap procedure. We calculate the optimal level of a product mix and measure the effectiveness provided by the interaction of LTC stand alone, deferred annuity and whole-life insurance. We compare the results obtained by the two approaches and find that a natural hedging strategy for LTC insurers is attainable with a product mix of LTC and annuities, but including low proportion of LTC.

About tests of the “simplifying” assumption for conditional copulas

Abstract We discuss the so-called “simplifying assumption” of conditional copulas in a general framework. We introduce several tests of the latter assumption for non- and semiparametric copula models. Some related test procedures based on conditioning subsets instead of point-wise events are proposed. The limiting distributions of such test statistics under the null are approximated by several bootstrap schemes, most of them being new. We prove the validity of a particular semiparametric bootstrap scheme. Some simulations illustrate the relevance of our results.

Bootstrap based uncertainty bands for prediction in functional kriging

The increasing interest in spatially correlated functional data has led to the development of appropriate geostatistical techniques that allow to predict a curve at an unmonitored location using a functional kriging with external drift model that takes into account the effect of exogenous variables (either scalar or functional). Nevertheless uncertainty evaluation for functional spatial prediction remains an open issue. We propose a semi-parametric bootstrap for spatially correlated functional data that allows to evaluate the uncertainty of a predicted curve, ensuring that the spatial dependence structure is maintained in the bootstrap samples. The performance of the proposed methodology is assessed via a simulation study. Moreover, the approach is illustrated on a well known data set of Canadian temperature and on a real data set of PM10 concentration in the Piemonte region, Italy. Based on the results it can be concluded that the method is computationally feasible and suitable for quantifying the uncertainty around a predicted curve. Supplementary material including R code is available online.

Standardized Parameters in Misspecified Structural Equation Models: Empirical Performance in Point Estimates, Standard Errors, and Confidence Intervals

In practice, models always have misfit, and it is not well known in what situations methods that provide point estimates, standard errors (SEs), or confidence intervals (CIs) of standardized structural equation modeling (SEM) parameters are trustworthy. In this article we carried out simulations to evaluate the empirical performance of currently available methods. We studied maximum likelihood point estimates, as well as SE estimators based on the delta method, nonparametric bootstrap (NP-B), and semiparametric bootstrap (SP-B). For CIs we studied Wald CI based on delta, and percentile and BCa intervals based on NP-B and SP-B. We conducted simulation studies using both confirmatory factor analysis and SEM models. Depending on (a) whether point estimate, SE, or CI is of interest; (b) amount of model misfit; (c) sample size; and (d) model complexity, different methods can be the one that renders best performance. Based on the simulation results, we discuss how to choose proper methods in practice.