cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57882/20240101-001
title: Fictional Expectations and the Global Media in the Greek Debt Crisis: A Topic Modeling Approach

description: Data appendix to: Volker Daniel/Magnus Neubert/Agnes Orban: Fictional Expectations and the Global Media in the Greek Debt Crisis: A Topic Modeling Approach, in: Jahrbuch für Wirtschaftsgeschichte / Economic History Yearbook 59 (2018)/2, S. 525-566. https://doi.org/10.1515/jbwg-2018-0018 We study the role of global media during the Greek debt crisis and relate it to the transmission of events on financial actors’ expectations. To identify news coverage about the Greek debt crisis, we apply topic modeling to a newly com- piled dataset of over 430,000 articles from The International New York Times and Financial Times from 2009 to 2015. We identify a Greek debt crisis topic and relate it to events concerning Greece during this time period. Our finding is that events are only relevant for financial markets when they are covered in the media, whereas events without media coverage have no effect. News coverage without immediate events is equally irrelevant for financial markets.
DOI: 10.57882/20240408-000
title: Georeferenced data on German population and population density in 1816

description: The shapefile contains estimates for population and area in 1816 of 93 German territories comprising 25 states or groups of small states and 67 higher-level administrative units in larger states. The latter consist of Regierungsbezirke in the Kingdom of Prussia, and Kreise, Landdrosteien etc. in other states. Population data are treated following the principles established by Fertig et al. (2018). The area covered refers to the nation state of 1871 without Alsace-Lorraine.
DOI: 10.57882/20240624-001
title: Die Geografie der deutschen Börsen im Wandel: Börsennotierte Firmen in Deutschland 1937

description: Datenanhang zu: Sibylle Lehmann-Hasemeyer/Carsten Burhop: Die Geografie der deutschen Börsen im Wandel (1913-37), in: Bankhistorisches Archiv 40 (2016)/1-2, S. 23-37.
DOI: 10.57882/20240624-000
title: The Berlin stock exchange and the geography of German stock markets in 1913

description: Multiple stock exchanges have been in operation in Germany since the nineteenth century. We provide new data on the number of listed firms, the market value and industry characteristics for the benchmark year 1913 to understand the geography of stock market listings. We find that large firms tended to be listed on the Berlin stock exchange while the regional stock exchanges were important hosts for small- and medium-sized firms. We show that the geographic distance between the stock exchange and corporate headquarters is negatively related to listing decisions. Data appendix to: Carsten Burhop/Sibylle Lehmann-Hasemeyer: The Berlin stock exchange and the geography of German stock markets in 1913, in: European Review of Economic History 20 (2016)/4, pp. 429–451, https://doi.org/10.1093/ereh/hew010.
DOI: 10.57882/20240723-000
title: Data Sources on the 19th and Early 20th Century German Capital Market. Challenges and Opportunities: A daily bank performance index 1920-1931

description: It is a share index derived from the daily prices of 39 banks listed on the Berlin Stock Exchange. The Excel file contains both the indices presented in the paper and the underlying raw data.
DOI: 10.57882/20240812-000
title: Taxation, regulation and the information efficiency of the Berlin stock exchange, 1892-1913

description: We calculated a daily stock market index for the Berlin Stock Exchange. Data appendix to: Sergey Gelman/Carsten Burhop: Taxation, regulation and the information efficiency of the Berlin stock exchange, 1892–1913, in: European Review of Economic History, 12 (2008)/1, S. 39–66, https://doi.org/10.1017/S1361491608002104.
DOI: 10.57882/20241218-000
title: Courland Census Microdata, 1803–1804

description: The database contains information on individuals coded from the 1803-1804 census lists of six towns in Courland. This cross-sectional data documents social, demographic and economic life in the multinational Baltic urban millieu. The original documents are kept at the Herder Institute in Marburg and at the Latvian State Historical Archives in Rīga. The data is provided as a comma separated CSV UTF-8 file along with a readme file. The database consists of 39 variables. First 30 variables adhere to coding standards defined in Siegfried Gruber's "Mosaic data files: Documentation of harmonized variables, Version 1.9 Mosaic 1.9" (https://assets.ipums.org/_files/mosaic/mosaic_data_files_harmonized_variables_version_1.9.pdf). Additional variables are: archive Data type: string. Name of the archives in which the source is held. LVVA for Latvian State Historical Archives. geonameid Data type: integer. Unique code of the place according to the Geonames gazetteer (see http://geonames.org). illness_or Data type: string. Diseases and disabilities of a person if specified in the source. notes Data type: string. Additional notes provided in the source. place_document Data type: string. German name of the place where a person was enumerated. place_origin Data type: string. Place of origin of a person as indicated in the source. ref_no Data type: string. Call number of the source. relig_or Data type: string. "Nationality" in the original wording. sheet_no Data type: string. Sheet number in the archival file in which the record was registered.
DOI: 10.57882/20250725-000
title: Dividend Policy: An Empirical Analysis for Imperial Germany

description: Data appendix to: Carsten Burhop/Felix Selgert: Dividend Policy: An Empirical Analysis for Imperial Germany, in: German Economic Review 22 (2025), https://doi.org/10.1515/ger-2024-0039.
DOI: 10.57882/20250925-001
title: Die Industrialisierung der Saarregion (1815–1914)

description: Datenanhang zu: Ralf Banken: Die Industrialisierung der Saarregion. Teil 1: Die Frühindustrialisierung 1815-1850. Stuttgart 2000 und Teil 2: Take-Off-Phase und Hochindustrialisierung 1850-1914. Stuttgart 2003.
DOI: 10.57882/20250925-000
title: Edelmetallmangel und Großraubwirtschaft: Die Entwicklung des deutschen Edelmetallsektors und die Degussa AG 1933–1945

description: Datenanhang zu: Ralf Banken: Edelmetallmangel und Großraubwirtschaft. Die Entwicklung des deutschen Edelmetallsektors und die Degussa AG 1933-1945. Berlin 2009.
DOI: 10.57882/20250925-002
title: Hitlers Steuerstaat: Die Steuerpolitik im Dritten Reich

description: Datenanhang zu: Ralf Banken: Hitlers Steuerstaat. Die Steuerpolitik im Dritten Reich. Berlin 2018.
DOI: 10.57882/20250925-003
title: Die quantitative Entwicklung des bundesdeutschen Einzelhandels 1949–2000: Daten
DOI: 10.57882/20250925-004
title: Die quantitative Entwicklung der bundesdeutschen Warenhäuser 1948–2000: Daten
DOI: 10.57882/20250929-000
title: The slow emergence of the rational investor: Grain markets and grain storage of rural estates in western Germany, eighteenth and early nineteenth centuries

description: We develop new datasets of monthly grain prices in 14 urban markets and of the storage and marketing of grain by five rural estates located in Western Germany between the late seventeenth century and c. 1860. We explore whether observed patterns of monthly prices, sales and storage of grain are consistent with the rational competitive storage model (RCSM). Results establish, first, that the crop year was divided into two seasons characterized by processing and marketing, respectively. Monthly prices of wheat during the marketing season (from February to summer) showed a pattern consistent with the rational competitive storage model. In the case of rye, which was a subsistence crop, a pattern of monthly prices consistent with the RCSM emerged only in the nineteenth century and was confined to urban markets. Second, grain price movements in spring and early summer predicted November prices, which implies the presence of inter-annual arbitrage. Whereas carry-over stocks held by rural estates partly served to provision a large household, tentative evidence suggests that some rural estates indeed also engaged in inter-annual arbitrage, but they do not seem to have been able to earn a profit from holding carryover stocks.
DOI: 10.6084/m9.figshare.28441512
title: Bayesian variable selection in distributed lag models: a focus on binary quantile and count data regressions

description: Distributed Lag Models (DLMs) and similar regression approaches such as MIDAS have been used for many decades in econometrics and more recently to investigate how poor air quality adversely affects human health. In this paper we describe how to expand the utility of these models for Bayesian inference by leveraging latent variables. In particular we explain how to perform binary regression to better handle imbalanced data, how to incorporate negative binomial regression, and how to estimate the probability of predictor inclusion. Extra parameters introduced through the DLM framework may require calibration for the MCMC algorithm, but this will not be the case in DLM-based analyses often seen in pollution exposure literature. In these cases, the parameters are inferred through a fully automatic Gibbs sampling procedure.
DOI: 10.6084/m9.figshare.28441512.v1
title: Bayesian variable selection in distributed lag models: a focus on binary quantile and count data regressions

description: Distributed Lag Models (DLMs) and similar regression approaches such as MIDAS have been used for many decades in econometrics and more recently to investigate how poor air quality adversely affects human health. In this paper we describe how to expand the utility of these models for Bayesian inference by leveraging latent variables. In particular we explain how to perform binary regression to better handle imbalanced data, how to incorporate negative binomial regression, and how to estimate the probability of predictor inclusion. Extra parameters introduced through the DLM framework may require calibration for the MCMC algorithm, but this will not be the case in DLM-based analyses often seen in pollution exposure literature. In these cases, the parameters are inferred through a fully automatic Gibbs sampling procedure.
DOI: 10.6084/m9.figshare.25250106
title: A variable clustering approach for overdispersed high-dimensional count data using a copula-based mixture model

description: In this paper, we propose a latent variable model for the analysis and clustering of high-dimensional correlated and overdispersed count data. We use a set of random effects to capture within-group correlations and a copula to address between-group correlations. Since maximum likelihood (ML) estimation is computationally intensive for high-dimensional data, we propose a fast moment-based estimation procedure. Additionally, we implement the proposed estimation procedure into a clustering algorithm, borrowing ideas from the <i>K</i>-means algorithm. Based on a simulation study, we demonstrate that the estimation procedure shows good stability and precision. Furthermore, it is computationally fast, unlike the ML approach which shows serious convergence issues. In addition, the clustering algorithm with our moment-based estimation procedure can identify the simulated grouped data structure. The approach is further illustrated with a high-dimensional RNA-Seq dataset of commercial potatoes.
DOI: 10.6084/m9.figshare.25250106.v1
title: A variable clustering approach for overdispersed high-dimensional count data using a copula-based mixture model

description: In this paper, we propose a latent variable model for the analysis and clustering of high-dimensional correlated and overdispersed count data. We use a set of random effects to capture within-group correlations and a copula to address between-group correlations. Since maximum likelihood (ML) estimation is computationally intensive for high-dimensional data, we propose a fast moment-based estimation procedure. Additionally, we implement the proposed estimation procedure into a clustering algorithm, borrowing ideas from the <i>K</i>-means algorithm. Based on a simulation study, we demonstrate that the estimation procedure shows good stability and precision. Furthermore, it is computationally fast, unlike the ML approach which shows serious convergence issues. In addition, the clustering algorithm with our moment-based estimation procedure can identify the simulated grouped data structure. The approach is further illustrated with a high-dimensional RNA-Seq dataset of commercial potatoes.
DOI: 10.6084/m9.figshare.22988869.v1
title: A sampling scheme for estimating the prevalence of a pandemic

description: The spread of COVID-19 makes it essential to investigate its prevalence. In such investigation research, as far as we know, the widely-used sampling methods didn’t use the information sufficiently about the numbers of the previously diagnosed cases, which provides a priori information about the true numbers of infections. This motivates us to develop a new, two-stage sampling method in this paper, which utilizes the information about the distributions of both population and diagnosed cases, to investigate the prevalence more efficiently. The global likelihood sampling, a robust and efficient sampler to draw samples from any probability density function, is used in our sampling strategy, and thus, our new method can automatically adapt to the complicated distributions of population and diagnosed cases. Moreover, the corresponding estimating method is simple, which facilitates the practical implementation. Some recommendations for practical implementation are given. Finally, several simulations and a practical example verify its efficiency.
DOI: 10.6084/m9.figshare.26347436
title: Adaptive seamless phase II/III design with sequential estimation-adjusted urn model

description: Clinical trials are an essential component of the drug development process, providing crucial data on the efficacy and safety of new treatments. However, traditional clinical trial designs can be inefficient and ineffective, leading to increased costs and a higher risk of failure. The FDA Oncology Center of Excellence (OCE) has recently initiated Project Optimus to reform the dose selection paradigm for oncology treatments. We propose the adaptive seamless phase II/III clinical trial designs (ASD) with the sequential estimation-adjusted urn (SEU) model for randomization to achieve efficient and ethical objectives. However, the combination of ASD and SEU poses a challenge in controlling the type I error rate: ASD exerts a dual influence of multiplicity and selection; all the responses and treatment assignments are not independent due to SEU. In this paper, we investigated how to overcome these difficulties, utilize the two adaptive approaches’ advantages, and control the type I error rate. We provide a theoretical foundation for this procedure, and numerical studies demonstrate that our methods can assign more people to better treatments, leading to fewer failures while still controlling the type I error rate and preserving power.
DOI: 10.6084/m9.figshare.26347436.v1
title: Adaptive seamless phase II/III design with sequential estimation-adjusted urn model

description: Clinical trials are an essential component of the drug development process, providing crucial data on the efficacy and safety of new treatments. However, traditional clinical trial designs can be inefficient and ineffective, leading to increased costs and a higher risk of failure. The FDA Oncology Center of Excellence (OCE) has recently initiated Project Optimus to reform the dose selection paradigm for oncology treatments. We propose the adaptive seamless phase II/III clinical trial designs (ASD) with the sequential estimation-adjusted urn (SEU) model for randomization to achieve efficient and ethical objectives. However, the combination of ASD and SEU poses a challenge in controlling the type I error rate: ASD exerts a dual influence of multiplicity and selection; all the responses and treatment assignments are not independent due to SEU. In this paper, we investigated how to overcome these difficulties, utilize the two adaptive approaches’ advantages, and control the type I error rate. We provide a theoretical foundation for this procedure, and numerical studies demonstrate that our methods can assign more people to better treatments, leading to fewer failures while still controlling the type I error rate and preserving power.
DOI: 10.6084/m9.figshare.22988869
title: A sampling scheme for estimating the prevalence of a pandemic

description: The spread of COVID-19 makes it essential to investigate its prevalence. In such investigation research, as far as we know, the widely-used sampling methods didn’t use the information sufficiently about the numbers of the previously diagnosed cases, which provides a priori information about the true numbers of infections. This motivates us to develop a new, two-stage sampling method in this paper, which utilizes the information about the distributions of both population and diagnosed cases, to investigate the prevalence more efficiently. The global likelihood sampling, a robust and efficient sampler to draw samples from any probability density function, is used in our sampling strategy, and thus, our new method can automatically adapt to the complicated distributions of population and diagnosed cases. Moreover, the corresponding estimating method is simple, which facilitates the practical implementation. Some recommendations for practical implementation are given. Finally, several simulations and a practical example verify its efficiency.
DOI: 10.6084/m9.figshare.26207067
title: On the construction of trend free constant block sum PBIB designs

description: Agricultural and industrial experimentation may often witness the effect of systematic trend. Trend effects, though remote, may still influence the response under consideration. The incorporation of trend effects in the model thus paves the way for proper model specification. Completely trend robust block designs, which are commonly known as trend-free block designs, are an important class of block designs in such a way that the analysis can be done in the usual manner as it could be done in case of normal block designs due to orthogonal nature of trend effects and treatment effects. This article deals with the construction of some classes of completely trend robust i.e. trend free constant block sum partially balanced incomplete block (TFCBS-PBIB) designs along with characterization properties. SAS macros have also been developed and made available in public domain to provide readymade solutions to the end-users.
DOI: 10.6084/m9.figshare.26207067.v1
title: On the construction of trend free constant block sum PBIB designs

description: Agricultural and industrial experimentation may often witness the effect of systematic trend. Trend effects, though remote, may still influence the response under consideration. The incorporation of trend effects in the model thus paves the way for proper model specification. Completely trend robust block designs, which are commonly known as trend-free block designs, are an important class of block designs in such a way that the analysis can be done in the usual manner as it could be done in case of normal block designs due to orthogonal nature of trend effects and treatment effects. This article deals with the construction of some classes of completely trend robust i.e. trend free constant block sum partially balanced incomplete block (TFCBS-PBIB) designs along with characterization properties. SAS macros have also been developed and made available in public domain to provide readymade solutions to the end-users.
DOI: 10.6084/m9.figshare.26395326
title: On the prediction of rare events when sampling from large data

description: When modeling rare events using logistic regression, independent samples of event occurrence (ones) and nonoccurrence (zeros) are commonly taken from large datasets in order to fit models efficiently. A deterministic offset may then be included in the model to compensate for that sampling method. We propose a more complex sampling approach using stratified sampling within the sets of ones and zeros to ensure that we may sample more zeros from strata of interest. This design may avoid situations in which a random sample of zeros fails to capture the range of a key covariate. We employ sampling weights along with stratum-specific intercepts to obtain unbiased estimates of the logistic regression coefficients (including the intercept) and their standard errors. We use simulation to show that this method provides unbiased parameter estimates comparable with those of maximum likelihood. We also illustrate an application of this method to wildland fire occurrence prediction in a study area in northwestern Ontario, Canada.
DOI: 10.6084/m9.figshare.26395326.v1
title: On the prediction of rare events when sampling from large data

description: When modeling rare events using logistic regression, independent samples of event occurrence (ones) and nonoccurrence (zeros) are commonly taken from large datasets in order to fit models efficiently. A deterministic offset may then be included in the model to compensate for that sampling method. We propose a more complex sampling approach using stratified sampling within the sets of ones and zeros to ensure that we may sample more zeros from strata of interest. This design may avoid situations in which a random sample of zeros fails to capture the range of a key covariate. We employ sampling weights along with stratum-specific intercepts to obtain unbiased estimates of the logistic regression coefficients (including the intercept) and their standard errors. We use simulation to show that this method provides unbiased parameter estimates comparable with those of maximum likelihood. We also illustrate an application of this method to wildland fire occurrence prediction in a study area in northwestern Ontario, Canada.
DOI: 10.6084/m9.figshare.25515298
title: Double sparsity garrotized kernel machine in high-dimensional partially linear model

description: Obtaining excellent prediction accuracy in the high-dimensional partially linear model is particularly important. However, it is difficult to achieve due to the complex relationship between nonparametric covariates and the response. Irrelevant covariates and unimportant data also commonly attenuate the prediction performance of the model. Further, high-dimensional data analysis is challenging for modern statistical studies. To overcome these difficulties, we propose the Double Sparsity Garrotized Kernel Machine (DSGKM) method with an efficient algorithm and its adjusted version for prediction. Specifically, we estimate the nonparametric components using the kernel machine technique, and impose L1-norm penalties simultaneously to select relevant covariates and retain the representative data in the final model. Besides, the convergence analysis of the adjusted algorithm is conducted. The advantages of our method are: (i) to sufficiently capture the complex relationship between nonparametric covariates and the response; (ii) to identify relevant covariates and select representative data; and (iii) to achieve higher computational efficiency, especially the situations when both parametric and nonparametric components are high-dimensional. Results on both simulated and real data show that the proposed method outperforms existing methods, even when outliers exist.