Łukasz SROKA, University of Economics, Katowice
2021 Scientific Papers of Silesian University of Technology. Organization and Management Series  
The development of technology has allowed creating and using the new, more complex computational tools in static and econometrics in recent years. Since then, resampling methods has become more popular techniques in estimating statistics from small samples. The aim of the article is to present and to compare the bootstrap and the jackknife methods in estimation of interested statistics with explaining and illustrating the usefulness and limitation in the context of using in econometric.
more » ... ethodology/approach: To compare and present the methods, data of the length of bicycle paths divided into 371 polish counties from 2019 was received from Local Data Bank. From the data three samples were randomly selected and used as bootstrap and jackknife samples. Using the bootstrap and the jackknife simulations confidential intervals of the searching statistics with standard error were calculated. Results obtained for the methods were compared and described. Research limitations/implications: An analysis of these methods will allow improving the efficiency and reducing the error in estimating confidence intervals for searching statistics. Findings: As presented in the article, both the methods can be used to estimate mean, however, slightly better results are provided by the bootstrap. Furthermore, confidence intervals for confidence level at 95% created by these methods cover the population mean for each sample randomly selected from the population. To estimate standard deviation the better option is to choose the bootstrap method. Although, both confidence intervals for confidence at level 95% cover the population standard deviation, the bootstrap methods perform more accurate results with a smaller standard deviation. Originality/value: It was proven that the bootstrap method is slightly better in estimation confidence intervals based on the skewed data in comparison with the jackknife method.
doi:10.29119/1641-3466.2021.153.31 fatcat:xmbzxwczzfa33l7klcpbsfisni