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The purpose of this paper is to present bootstrapping as an alternative statistical methodology to analyze time studies and input data for discrete‐event simulations. Bootstrapping is a non‐parametric technique to estimate the sampling distribution of a statistic by doing repeated sampling (i.e. resampling) with replacement from an original sample. This paper proposes a relatively simple implementation of bootstrap techniques to time study analysis.

Using an inductive approach, this work selects a typical situation to conduct a time study, applies two bootstrap procedures for the statistical analysis, compares bootstrap to traditional parametric approaches, and extrapolates general advantages of bootstrapping over parametric approaches.

Bootstrap produces accurate inferences when compared to those from parametric methods, and it is an alternative when the underlying parametric assumptions are not met.

Research results contribute to work measurement and simulation fields since bootstrap promises an increase in accuracy in cases where the normality assumption is violated or only small samples are available. Furthermore, this paper shows that electronic spreadsheets are appropriate tools to implement the proposed bootstrap procedures.

In previous work, the standard procedure to analyze time studies and input data for simulations is a parametric approach. Bootstrap permits to obtain both point estimates and estimates of time distributions. Engineers and managers involved in process improvement initiatives could use bootstrap to exploit better the information from available samples.

Quality function deployment (QFD) is a planning methodology to improve products, services and their associated processes by ensuring that the voice of the customer has been effectively deployed through specified and prioritised technical attributes (TAs). The purpose of this paper is two ways: to enhance the prioritisation of TAs: computer simulation significance test; and computer simulation confidence interval. Both are based on permutation sampling, bootstrap sampling and parametric bootstrap sampling of given empirical data.

The authors present a theoretical case for the use permutation sampling, bootstrap sampling and parametric bootstrap sampling. Using a published case study the authors demonstrate how these can be applied on given empirical data to generate a theoretical population. From this the authors describe a procedure to decide upon which TAs have significantly different priority, and also estimate confidence intervals from the theoretical simulated populations.

First, the authors demonstrate not only parametric bootstrap is useful to simulate theoretical populations. The authors can also employ permutation sampling and bootstrap sampling to generate theoretical populations. Then the authors obtain the results from these three approaches. qThe authors describe why there is a difference in results of permutation sampling, bootstrap and parametric bootstrap sampling. Practitioners can employ any approach, it depends how much variation in FWs is required by quality assurance division.

Using these methods provides QFD practitioners with a robust and reliable method for determining which TAs should be selected for attention in product and service design. The explicit selection of TAs will help to achieve maximum customer satisfaction, and save time and money, which are the ultimate objectives of QFD.

While crop insurance ratemaking has been studied for many decades, it is still faced with many challenges. Crop insurance premium rates (PRs) are traditionally determined only by point estimation, and this approach may lead to uncertainty because it is sensitive to the underwriter’s assumptions regarding the trend, yield distribution, and other issues such as data scarcity and credibility. Thus, the purpose of this paper is to obtain the interval estimate for the PR, which can provide additional information about the accuracy of the point estimate.

A bootstrap method based on the loss cost ratio ratemaking approach is proposed. Using Monte Carlo experiments, the performance of this method is tested against several popular methods. To measure the efficiency of the confidence interval (CI) estimators, the actual coverage probabilities and the average widths of these intervals are calculated.

The proposed method is shown to be as efficient as the non-parametric kernel method, and has the features of flexibility and robustness, and can provide insight for underwriters regarding uncertainty based on the width of the CI.

Comprehensive comparisons are conducted to show the advantage and the efficiency of the proposed method. In addition, a significant empirical example is given to show how to use the CIs to support ratemaking.

We provide a new characterization of the equality of two positive-definite matrices A and B, and we use this to propose several new computationally convenient statistical tests for the equality of two unknown positive-definite matrices. Our primary focus is on testing the information matrix equality (e.g. White, 1982, 1994). We characterize the asymptotic behavior of our new trace-determinant information matrix test statistics under the null and the alternative and investigate their finite-sample performance for a variety of models: linear regression, exponential duration, probit, and Tobit. The parametric bootstrap suggested by Horowitz (1994) delivers critical values that provide admirable level behavior, even in samples as small as n = 50. Our new tests often have better power than the parametric-bootstrap version of the traditional IMT; when they do not, they nevertheless perform respectably.

The improvement in the efficiency of public management leads to an increase in the quality of services, boosting confidence in institutions and thus generating a multiplier effect. This paper aims to update the literature on the data envelopment analysis (DEA), which uses a frontier methodology, for measuring the efficiency applied to the defence sector.

Two main research areas are related through this study: Defence Economics and Efficiency and Productivity Analysis. The frontier methods for measuring technical efficiency are grouped into parametric, non-parametric and semi-parametric. The DEA literature review in the selected sector allows to identify three main fields of work that are discussed in detail.

Existing empirical research has hardly applied bootstrapping methods to remove bias from the estimates. No empirical work has applied an analysis of efficiency determinants by the inclusion of exogenous variables.

The managerial implications of efficiency are numerous. A line of research is proposed to examine the extent to which efficiency improvements have an impact on reputation and, therefore, on the image of and confidence in institutions. The link between Defence Economics and Corruption Economics is also considered.

La mejora de la eficiencia de la gestión pública conduce a un aumento de la calidad de los servicios, impulsando la confianza en las instituciones y generando así un efecto multiplicador. El presente trabajo tiene por objeto actualizar la literatura sobre la metodología frontera de medición de la eficiencia DEA, aplicada al sector de la Defensa.

A través de este estudio, se relacionan dos áreas principales de investigación: Economía de la Defensa y Análisis de Eficiencia y Productividad. Los métodos frontera para medir la eficiencia técnica se agrupan en paramétricos, no paramétricos y semiparamétricos. La revisión de la literatura DEA en el sector seleccionado permite identificar tres campos de trabajo principales que se discuten en detalle.

La investigación empírica existente apenas ha aplicado métodos bootstrap para eliminar el sesgo de las estimaciones. Ningún trabajo empírico ha aplicado un análisis de determinantes de la eficiencia mediante la inclusión de variables exógenas.

Las repercusiones de la eficiencia en la gestión son numerosas. Se propone una línea de investigación para examinar en qué medida las mejoras de la eficiencia repercuten en la reputación y, por consiguiente, en la imagen y la confianza en las instituciones. También se considera el vínculo entre la Economía de la Defensa y la Economía de la Corrupción.

Statistical inference (estimation and testing) for the stochastic volatility (SV) model Taylor (1982, 1986) is challenging, especially likelihood-based methods which are difficult to apply due to the presence of latent variables. The existing methods are either computationally costly and/or inefficient. In this paper, we propose computationally simple estimators for the SV model, which are at the same time highly efficient. The proposed class of estimators uses a small number of moment equations derived from an ARMA representation associated with the SV model, along with the possibility of using “winsorization” to improve stability and efficiency. We call these ARMA-SV estimators. Closed-form expressions for ARMA-SV estimators are obtained, and no numerical optimization procedure or choice of initial parameter values is required. The asymptotic distributional theory of the proposed estimators is studied. Due to their computational simplicity, the ARMA-SV estimators allow one to make reliable – even exact – simulation-based inference, through the application of Monte Carlo (MC) test or bootstrap methods. We compare them in a simulation experiment with a wide array of alternative estimation methods, in terms of bias, root mean square error and computation time. In addition to confirming the enormous computational advantage of the proposed estimators, the results show that ARMA-SV estimators match (or exceed) alternative estimators in terms of precision, including the widely used Bayesian estimator. The proposed methods are applied to daily observations on the returns for three major stock prices (Coca-Cola, Walmart, Ford) and the S&P Composite Price Index (2000–2017). The results confirm the presence of stochastic volatility with strong persistence.

This paper aims to evaluate the performance of basic metals industry in India and analyze its determinants, using data envelopment analysis (DEA) method. It also intends to compare the results through conventional two-stage and bootstrap-based inferences.

Considering technical efficiency as a measure of performance, this paper specifically investigates whether the participation of a firm in the global market affects its performance. The conventional two-stage procedure is used to test the export intensity and firm performance nexus. The bootstrap-based algorithms (by Simar and Wilson, 2007) are used to correct the bias and serial correlation issues involved in the conventional approach.

The result shows a negative relation between export intensity and firm performance while following the conventional procedure. Even after accounting for serial correlation, the relation remains more or less similar to that of conventional analysis. However, a strong negative relation between export intensity and firm performance is not observed in a more reliable inference obtained after correcting for possible bias as well as serial correlation.

This paper is based on cross-sectional analysis, and a more reliable result can be obtained by considering a larger sample and longer period.

This paper shows how the conventional two-stage procedure may result in misleading inferences due to bias in the estimation of efficiency scores and the serial correlation during the second stage inferential analysis. This paper also empirically exemplifies how the double bootstrap DEA procedure can overcome these limitations of the conventional two-stage approach.

The purpose of this paper is to estimate and analyse the technical efficiency (TE) component of productivity for a sample of winegrowers from the Douro Demarcated Region in Portugal.

The data were collected through face-to-face surveys and includes a sample of 110 farmers’ vineyards with specific input-output information and other data about production systems during the year of 2017. The authors use a two-stage data envelopment analysis using bootstrap techniques to obtain TE scores in the farmers’ vineyards and to examine the determinants of its efficiency.

The results show that some farmers’ vineyards have a low efficiency level and that there are essential determinants of the production system, which can influence its efficiency. This suggests considerable opportunities for improvement of wine grape productivity through better use of available resources considering the state of technology.

This work has overcome the lack of data in the farmers’ vineyards, the lack of efficiency studies in the region and also allowed to evaluate the production systems and to assess their impact on efficiency.

The main purpose of this paper is to determine the bias-corrected efficiencies and rankings of the selected hotels and restaurants (H&Rs) in India.

The data for the Indian H&R sector are collected from the Prowess database. The bootstrap data envelopment analysis (DEA) based on a constant return to scale (CRS), variable return to scale-input oriented (VRS-IP) and variable return to scale-output oriented (VRS-OP) are applied on H&Rs to obtain the bias-corrected efficiencies.

It is found that relative efficiencies using basic DEA methods of all the 45 H&Rs of India are overestimated. These efficiencies are corrected using bias correction through bootstrap DEA methods. The bounds for the efficiencies of each H&R are computed using all the adopted methods. All H&Rs are ranked using bias-corrected efficiencies, and the linear trend between ranks suggests that the H&Rs are ranked almost similarly by all the adopted methods.

To improve efficiency, Indian H&R companies must rethink their personnel needs by enhancing their workforce management capabilities. The government needs to extend more support to this sector by introducing a liberal legislation framework and supporting infrastructure policies.

There is a paucity of studies on H&Rs in India. The current study focused on measuring bias-corrected efficiencies of the selected H&Rs of India. This study is one of the few initiatives to explore bias-corrected efficiencies extensively using the bootstrap DEA method.

The paper analyzes the relative technical efficiency of the transition economies of the Western Balkans in the period 2007–2021, in comparison with the former countries with a socialist state system, today members of the European Union (EU), based on selected macroeconomic indicators and panel data.

Data envelopment analysis (DEA), i.e. its extension, DEA Window analysis, is applied. Total technical efficiency, as a prerequisite of economic efficiency, is decomposed into pure technical efficiency (PTE) and scale efficiency (SE). Bootstrapping method and Mann–Whitney U test were used to check the robustness of the obtained results, i.e. efficiency values.

The results show that in 2020, all observed countries recorded a significant drop in economic efficiency as a result of a general, disproportionate drop in the value of selected macroeconomic variables, which occurred due to the global economic crisis and the slowdown in economic activity caused by the COVID-19 pandemic. This drop in efficiency was significantly greater in the former socialist states, now members of the European Union, which showed their greater sensitivity to global crises. None of the observed economies in the observed period was relatively efficient, that is, at the level of best practice, which occurred primarily as a consequence of the inefficiency of business conditions expressed in the economies of scale.

The main limitation of this study stems from the very nature of the concept of DEA efficiency, which is relative in nature. Also, the results and their interpretation are also significantly influenced by the choice of model variables, as shown by Lábaj et al. (2013), as well as a small number of decision-making units (DMUs). The mentioned limitations prevent unambiguous interpretation and generalization of the obtained results.

The study may be of importance to economic policy makers in macroeconomic decision-making. The application of the DEA concept in measuring the technical efficiency of national economies is a useful tool in the analysis of macroeconomic performance and a benchmarking approach for positioning and achieving competitive advantage on the international market.

Since research of this type is very limited, the results of this study make a theoretical and empirical contribution to the literature, creating a basis for future research and reexamination. The application of the DEA concept in measuring the technical efficiency of national economies is a useful tool in the analysis of macroeconomic performance and a benchmarking approach for positioning and achieving competitive advantage in the international market.