spGARCH: An R-Package for Spatial and Spatiotemporal ARCH models

In this paper, a general overview on spatial and spatiotemporal ARCH models is provided. In particular, we distinguish between three different spatial ARCH-type models. In addition to the original definition of Otto et al. (2016), we introduce an exponential spatial ARCH model in this paper. For this new model, maximum-likelihood estimators for the parameters are proposed. In addition, we consider a new complex-valued definition of the spatial ARCH process. From a practical point of view, the use of the R-package spGARCH is demonstrated. To be precise, we show how the proposed spatial ARCH models can be simulated and summarize the variety of spatial models, which can be estimated by the estimation functions provided in the package. Eventually, we apply all procedures to a real-data example

Small numbers matching markets: Unstable and inefficient due to over-competition?

The extant literature on matching markets assumes ordinal preferences for matches, while bargaining within matches is mostly excluded. Central for this paper, however, is the bargaining over joint profits from potential matches. We investigate, both theoretically and experimentally, a seemingly simple allocation task in a 2x2 market with repeated negotiations. More than 75% of the experimental allocations are unstable, and 40% of the matches are inefficient (in cases where inefficiency is possible). By defining the novel concept 'altruistic core', we can explain the occurrence of inefficient matches as well as the significant behavioral differences among our six treatments. --matching market,price negotiation,optimal allocation,altruism

Spatial autoregressive fractionally integrated moving average model

In this paper, we introduce the concept of fractional integration for spatial autoregressive models. We show that the range of the dependence can be spatially extended or diminished by introducing a further fractional integration parameter to spatial autoregressive moving average models (SARMA). This new model is called the spatial autoregressive fractionally integrated moving average model, briefly sp-ARFIMA. We show the relation to time-series ARFIMA models and also to (higher-order) spatial autoregressive models. Moreover, an estimation procedure based on the maximum-likelihood principle is introduced and analysed in a series of simulation studies. Eventually, the use of the model is illustrated by an empirical example of atmospheric fine particles, so-called aerosol optical thickness, which is important in weather, climate and environmental science

Framework für die Nutzenargumentation des Produktinformationsmanagements

Zusammenfassung: Effizientes und effektives Produktinformationsmanagement (PIM) ist einer der Erfolgsfaktoren für moderne Geschäftsmodelle. Als wesentliche Teilaufgabe des Enterprise Content Management bildet es im Unternehmen eine Querschnittsfunktion, von der verschiedene Unternehmensbereiche profitieren: von der Beschaffung über die Lagerhaltung und Produktion bis zum Service. Gerade aus diesem Grund treten in den Unternehmen jedoch Schwierigkeiten bei der Identifikation und Quantifizierung der Nutzenpotenziale von PIM auf. Für die Nutzenanalyse ist eine PIM-Initiative an Geschäftstreiber zu koppeln, weil nur dadurch der Nutzen transparent gemacht werden kann. Dabei wird deutlich, dass die Verbesserung der Produktinformationsqualität als zentrales Ziel von PIM positiv auf einzelne Nutzendimensionen (Kosten, Zeit, Qualität und Umsatz) wirkt. Mithilfe des dargestellten Frameworks können in der Praxis schnell und einfach geeignete Nutzenpotenziale für die PIM-Initiative im eigenen Unternehmen identifiziert werden. Diese wiederum bilden die Grundlage einer Wirtschaftlichkeitsbetrachtung, die erforderlich ist, um eine PIM-Investition zu rechtfertige

Impact of academic authorship characteristics on article citations

Scientific self-evaluation practices are increasingly built on citation counts. Citation practices for the top journals in economics, psychology, and statistics illustrate article characteristics that influence citation frequencies. Citation counts differ between the investigated disciplines, with economics attracting the most citations and statistics the least. Although articles in statistics are cited less frequently, its proportion of uncited articles is the smallest of all three disciplines. Academic authorship characteristics clearly influence the number of citations. Having authors alphabetically ordered, a practice differently present in the investigated disciplines, increases citations. Further, the more authors there are, the more the article is cited, and a first author with a common surname has positive effects on citation counts, whereas two or more authors sharing a surname attracts fewer citations. In addition, the shorter the article’s title, the higher the number of citations

Online network monitoring

An important problem in network analysis is the online detection of anomalous behaviour. In this paper, we introduce a network surveillance method bringing together network modelling and statistical process control. Our approach is to apply multivariate control charts based on exponential smoothing and cumulative sums in order to monitor networks generated by temporal exponential random graph models (TERGM). The latter allows us to account for temporal dependence while simultaneously reducing the number of parameters to be monitored. The performance of the considered charts is evaluated by calculating the average run length and the conditional expected delay for both simulated and real data. To justify the decision of using the TERGM to describe network data, some measures of goodness of fit are inspected. We demonstrate the effectiveness of the proposed approach by an empirical application, monitoring daily flights in the United States to detect anomalous patterns. © 2021, The Author(s)

A general framework for spatial GARCH models

In time-series analysis, particularly in finance, generalized autoregressive conditional heteroscedasticity (GARCH) models are widely applied statistical tools for modelling volatility clusters (i.e., periods of increased or decreased risk). In contrast, it has not been considered to be of critical importance until now to model spatial dependence in the conditional second moments. Only a few models have been proposed for modelling local clusters of increased risks. In this paper, we introduce a novel spatial GARCH process in a unified spatial and spatiotemporal GARCH framework, which also covers all previously proposed spatial ARCH models, exponential spatial GARCH, and time-series GARCH models. In contrast to previous spatiotemporal and time series models, this spatial GARCH allows for instantaneous spill-overs across all spatial units. For this common modelling framework, estimators are derived based on a non-linear least-squares approach. Eventually, the use of the model is demonstrated by a Monte Carlo simulation study and by an empirical example that focuses on real estate prices from 1995 to 2014 across the postal code areas of Berlin. A spatial autoregressive model is applied to the data to illustrate how locally varying model uncertainties (e.g., due to latent regressors) can be captured by the spatial GARCH-type models

Generalized Spatial and Spatiotemporal ARCH Models

In time-series analyses, particularly for finance, generalized autoregressive conditional heteroscedasticity (GARCH) models are widely applied statistical tools for modelling volatility clusters (i.e., periods of increased or decreased risk). In contrast, it has not been considered to be of critical importance until now to model spatial dependence in the conditional second moments. Only a few models have been proposed for modelling local clusters of increased risks. In this paper, we introduce a novel spatial GARCH process in a unified spatial and spatiotemporal GARCH framework, which also covers all previously proposed spatial ARCH models, exponential spatial GARCH, and time-series GARCH models. In contrast to previous spatiotemporal and time series models, this spatial GARCH allows for instantaneous spill-overs across all spatial units. For this common modelling framework, estimators are derived based on a non-linear least-squares approach. Eventually, the use of the model is demonstrated by a Monte Carlo simulation study and by an empirical example that focuses on real estate prices from 1995 to 2014 across the ZIP-Code areas of Berlin. A spatial autoregressive model is applied to the data to illustrate how locally varying model uncertainties (e.g., due to latent regressors) can be captured by the spatial GARCH-type models