Physica A: Statistical Mechanics and its Applications

This dataset contains firm-size distributions for mid-Victorian era from the 1851-1881 England and Wales censuses and corresponds to research data supporting "A tale of two tails: Do Power Law and Lognormal models fit firm-size distributions in the mid-Victorian era?" by Montebruno, P., Bennett, R, van Lieshout, C., and Smith, H. as an outcome of the ESRC project ES/M010953: Drivers of Entrepreneurship and Small Businesses lead by PI Prof. Robert J. Bennett. The material consists of one raw text file with eight variables: 1. Frequency distribution of 1851 2. Frequency distribution of 1861 3. Frequency distribution of 1871 4. Frequency distribution of 1881 5. Size distribution of 1851 6. Size distribution of 1861 7. Size distribution of 1871 8. Size distribution of 1881 A detailed explanation of how these distributions were obtained and how to use them in the context of firm-size distribution analysis including the distinction between Power Law and Lognormal behaviour in the tails can be found in the paper "A tale of two tails: Do Power Law and Lognormal models fit firm-size distributions in the mid-Victorian era?" by Montebruno, P., Bennett, R, van Lieshout, C., and Smith, H. published in the journal Physica A: Statistical Mechanics and its Applications. The file can be opened in any text editor, database management system (Access) or statistical package (Stata, SPSS). This dataset should be cited as Montebruno, P., Bennett, R., van Lieshout, C., and Smith, H. “Research data supporting "A tale of two tails: Do Power Law and Lognormal models fit firm-size distributions in the mid-Victorian era?"” Mendeley Data. http://dx.doi.org/10.17632/86xkkncmw3.1

Replication files for The exponential Pareto model with hidden income processes: evidence from Chile

384 examples of walking distances, as computed using the different methods discussed in the paper.

lane-changing model's raw data

The data enclosed was used in the study and include daily data for decile portfolios and industries.

A proof of concept of a method that improves reconstructing the network / graph structure from experimental data using entropy bias estimation.

The data for this study consist of the daily opening, highest, lowest and closing prices of 10 industry indices, including the energy industry index (EII), raw material industry index (RMII), industrial sector index (ISI), optional consumer industry index (OCII), major consumer industry index (MCII), medical and health industry index (MHII), financial real estate industry index (FEII), information technology industry index (ITII), telecom business industry index (TBII) and utilities industry index (UII) of the Shanghai stock exchange (SSE). The Shanghai Stock Exchange Industry Index can reflect the overall performance of the stocks of companies in different sectors of the Shanghai stock market and provide a target for the development of indexed investment products, especially ETF. The base period was December 31, 2013 with a base point of 1000, which was started in January 9, 2009. The sample period is January 9, 2009 to June 29, 2018 and includes a total of 2303 groups of daily data. These data sets were extracted from the Wind information database. The rates of returns are calculated from yesterday’s and today’s closing prices in the form of a logarithmic expression. The realized range fluctuation rates are calculated using the range estimation method based on the stochastic volatility model.

spreads for EU countries

Brazilian welfare program payments dataset files provided by Transparency Portal. These data were submited to a Newcomb-Benford Law (NBL)-based method for fraud detection.

This zipped file contains the program of solving the inequality constrained optimization problem of Eq.(A.6) by the interior point function methods (Matlab code).