In the current paper, we investigate the bias introduced through the calendar time sampling of the price process of financial assets. We analyze results from a Monte Carlo simulation which point to the conclusion that the multitude of jumps reported in the literature might be, to a large extent,...

The first large scale (almost 500 products, 64,300 individual price listings) empirical study of price dispersion based on the data from the Czech Republic’s e‑shops is presented in the paper. First, simple descriptive indicators like average, max, min, median and standard deviation were...

This research aims to study the nature of the relationship between the government spending and the private investment in the non-oil sectors of the Saudi economy through testing the crowding out effect during four decades and using the Bootstrapping technique. Firstly we use the Box-Cox...

Encouraging employees to share their creative ideas has been widely acknowledged as a crucial factor in enhancing organizational effectiveness. This research examines whether employees' idea sharing depends on leadership behavior and how organizational identification and culture explain the...

This paper proposes a new bootstrap procedure for mean squared errors of robust smallarea estimators. We formally prove the asymptotic validity of the proposed bootstrap method and examine its finite sample performance through Monte Carlo simulations. The results show that our procedure performs...

More than 25 years after German reunification, key economic indicators for households living in eastern German regions are still below the western German levels. This particularly holds for private net wealth, which reaches only about 40% of the western German level. However, a more granular...

The computing time for Markov Chain Monte Carlo (MCMC) algorithms can be prohibitively large for datasets with many observations, especially when the data density for each observation is costly to evaluate. We propose a framework where the likelihood function is estimated from a random subset of...

We propose a generic Markov Chain Monte Carlo (MCMC) algorithm to speed up computations for datasets with many observations. A key feature of our approach is the use of the highly efficient difference estimator from the survey sampling literature to estimate the log-likelihood accurately using...

We propose a generic Markov Chain Monte Carlo (MCMC) algorithm to speed up computations for datasets with many observations. A key feature of our approach is the use of the highly efficient difference estimator from the survey sampling literature to estimate the log-likelihood accurately using...