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Statistical model selection and evaluation methods like Akaike information criteria (AIC) and Monte Carlo simulation (MCS) have often established efficient output for reliability analysis with large sample size. Information criterion can provide better model selection and evaluation in small sample sizes setup by considering the well-known measure of bootstrap resampling. Our purpose is to utilize the capabilities of bootstrap resampling in information criterion to check for uncertainty arising from model selection as well as statistics of interest for small sample size using reliability analysis. In this study, therefore, a unique and efficient simulation scheme is proposed which contemplates the best model selection devised from efficient bootstrap simulation or variance reduced bootstrap information criterion to be combined with reliability analysis. It is beneficial to compute the spread of reliability values as against solitary fixed values with desirable statistics of interest for uncertainty analysis. The proposed simulation scheme is verified using a number of sample size focused response functions under repetitions-centred approach with AIC-based reliability analysis for comparison and MCS for accuracy. The results show that the proposed simulation scheme aids the statistics of interest by reducing the spread and hence the uncertainty in sample size-based reliability analysis when compared with conventional methods.

This article proposes consistent nonparametric methods for testing the null hypothesis of Lorenz dominance. The methods are based on a class of statistical functionals defined over the difference between the Lorenz curves for two samples of welfare-related variables. We present two specific test statistics belonging to the general class and derive their asymptotic properties. As the limiting distributions of the test statistics are nonstandard, we propose and justify bootstrap methods of inference. We provide methods appropriate for case where the two samples are independent as well as the case where the two samples represent different measures of welfare for one set of individuals. The small sample performance of the two tests is examined and compared in the context of a Monte Carlo study and an empirical analysis of income and consumption inequality.

This study revisits the widely used assumptions in long-term asset allocation: the normal distribution of long-horizon returns and the negligible impacts of estimation errors on the expected returns. This study uses the innovative simulation method of Fama and French (2018) for horizons of up to 30 years. The data in use are the U.S. value-weighted market returns of stocks, Treasury bonds, Treasury bills, commodities, and real estate investment trusts (REITs) for the 1970-2018 period. Distributions of continuously compounded returns from the 10-year horizon are normal across asset classes. Stock return distribution has the slowest rate of convergence to normality among groups of assets. Estimation errors of the expected monthly returns or annual returns are negligible relative to the standard deviation of the unexpected return. As the imprecisions persist over the investment horizons, the estimation errors of the monthly return have a strong effect on the variability of long-term asset returns. This study has significant implications for academics and investors based on the commonly accepted assumptions of long-term asset allocation.

Background: Many retirees in South Africa face the challenge of either outliving their retirement savings or living below their means. Studies suggest a ‘safe’ withdrawal rate of between 4% and 5%, which is below the average fund size-weighted drawdown rate of approximately 6.66%.Aim: To provide a scientific basis for the success rate of a ‘hybrid’ retirement strategy, whereby a retiree invests a proportion of their savings in a life annuity and the remaining proportion in a living annuity, to increase the success rate for South African retirees.Setting: Historical asset class returns (equities, bonds and inflation) for South Africa were sourced for the period 1900–2020.Method: Bootstrap sampling of historical asset returns was employed to simulate 10 000 random scenarios to investigate the success rate of various compositions of the ‘hybrid’ retirement strategy.Results: The success rate of all ‘hybrid’ portfolio compositions is significantly greater than the success rate of a pure living annuity when the withdrawal rate is less than 8%.Conclusion: In a South African context, a ‘hybrid’ retirement portfolio increases the probability of success for retirees withdrawing less than 8% from their portfolio – which constitutes approximately 50% of the current annuatised population – and may increase the inheritance of a retiree’s heir.Contribution: Where other studies have focussed solely on the success rate of a living annuity, we have shown that a ‘hybrid’ retirement strategy increases a South African retiree’s likelihood of retiring successfully when the withdrawal rate is less than 8%, which is approximately 50% of the annuatised population.

PurposeThis study explores the “Sell-in-May” effect in environmental, social and governance (ESG) indices and compares the seasonal effects in ESG equity indices with conventional equity indices.Design/methodology/approachThe authors use ordinary least squares (OLS) models and M-estimation as a robustness check, as OLS estimates may be sensitive to outliers. The authors also employ bootstrap simulations to use the data efficiently and to test whether seasonal trading strategies can produce abnormal returns.FindingsThe regression results reveal that seasonal effects in USA ESG equity indices are similar to those in conventional equity indices. Higher returns are noticeable from November through April, mainly in ESG indices including small and medium capitalization stocks. When the authors extend the Sell-in-May strategy from October through April, the authors find that the seasonal effect is significant for multiple ESG indices, even after accounting for the January effect. Bootstrap simulations show that the Sell-in-May and Extended Sell-in-May strategies appear to beat a buy-and-hold strategy on a risk-adjusted basis and that this result is stronger in medium and small capitalization ESG indices.Originality/valueAlthough previous research has considered the effectiveness of seasonal equity trading strategies and the general performance of ESG stocks, this is the first study to specifically examine the “Sell in May” effect in ESG indices. The authors also consider an “Extended” Sell-in-May strategy where stocks are purchased one month earlier and show that the strategy produces higher returns.

This paper examines the symmetric and asymmetric causal relationships between tourism and inclusive growth in Turkey and Nigeria over the period 1995Q1-2018Q4. The study employs a bootstrap simulation method with leverage adjustments to achieve the objective of the study. The method is used to see whether positive or negative tourism shocks cause inclusive growth and whether positive or negative inclusive growth shocks cause tourism activity. The results show no evidence of asymmetric causality between tourism and inclusive growth, while there is evidence of symmetric causality running from tourism to inclusive growth in Turkey. On the other hand, there is neither symmetric nor asymmetric causal relationship between tourism and inclusive growth in Nigeria. In sum, both neutrality and tourism-led growth hypothesis hold in Turkey, while Nigeria gives credence to neutrality hypothesis. The recommendations coming from the findings are that the tourism sector in both countries, Nigeria in particular, should be repositioned for better performance and effectiveness in stimulating inclusive growth. Rather than focusing on pro-poor and micro-based tourism policies that favour selected communities and localities, tourism should be included in development plans nationally, in order to ensure wider participation and more encompassing trickle-down effects on the citizenry. Furthermore, both countries should implement policies that will stimulate their tourism sectors for a larger and more significant contribution to real GDP.

The development of modern society cannot be imagined without electricity as a ubiquitous and almost irreplaceable energy source. Energy is a key factor in human development, it provides a standard of living and enables the economy to grow, with electricity being one of its most important forms. In the modern world, efficient energy supply, particularly electricity as its most flexible, commercial and cleanest form represents an important basis for economic growth and development. Although research of the causal link between electricity consumption, financial development and economic growth has been represented in the scientific literature for the last 20-30 years, the results are still contradictory. However, this particular topic has not been systematically investigated and addressed in Croatia. Therefore, studying the causal relationship and economic effects of the mentioned variables represents an important and challenging research task. The purpose of this paper is to determine the interconnectedness of electricity consumption, financial development and economic activity in Croatia. This will be achieved by analysing the available data for the last two and a half decades using the so-called bootstrap approach. According to the empirical results, causality runs from real GDP to total electricity consumption and from financial development to real GDP. In addition to empirical results, policy implications and recommendations for future research will also be presented in the paper.

Alternative models of productivity predict a range of its determinants besides that of research and development (R&D). We investigate the robustness of R&D vis-à-vis a dozen productivity determinants in a panel of 16 Organisation for Economic Co-operation and Development countries through panel cointegration, bootstrap simulations and extensive sensitivity tests. Domestic knowledge stocks, international knowledge diffusion and human capital remain robust across all measures. The cross-country differences in accumulated knowledge stocks and human capital appear to explain productivity differences across countries.

The indices C p and C pk are extensively used to assess process capability. However, they only take into account the process mean and standard deviation, but not the proximity of the process mean to the target value, T, of the process characteristic. Cpm does take into account the proximity of the process mean to the target value. We propose a method for selecting or judging the better of two suppliers or processes based on a confidence interval for the ratio C pm1 /C pm2 . Four methods of obtaining approximate confidence intervals are presented and compared, one based on the statistical theory given in Boyles (1991) and three based on the bootstrap, (referred to as SB (standard bootstrap), PB (percentile bootstrap), and BCPB (biased-corrected percentile bootstrap)). The performance was compared using simulation, which showed that, in two independent and normal process environments, Boyles's (1991) confidence interval and the SB confidence interval are more reliable than the PB and BCPB methods. A sample size of greater than 50 is recommended for selecting the most capable of two suppliers or processes.

The problem of evaluating the goodness of statistical models is investigated from an information-theoretic point of view. Information criteria are proposed for evaluating models constructed by various estimation procedures when the specified family of probability distributions does not contain the distribution generating the data. The proposed criteria are applied to the evaluation of models estimated by maximum likelihood, robust, penalised likelihood, Bayes procedures, etc. We also discuss the use of the bootstrap in model evaluation problems and present a variance reduction technique in the bootstrap simulation.