Philipp Otto

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University of Glasgow | UofG · School of Mathematics and Statistics

In this paper, we introduce a new spatial model that incorporates heteroscedastic variance depending on neighbouring locations. The proposed process is considered as the spatial equivalent to the temporal autoregressive conditional heteroscedasticity (ARCH) model. We also show how the newly introduced spatial ARCH model can be used in spatiotempora...

In this paper, we propose a two-stage LASSO estimation approach for the estimation of a full spatial weights matrix of spatiotemporal autoregressive models. In addition, we allow for an unknown number of structural breaks in the local means of each spatial location. These locally varying mean levels, however, can easily be mistaken as spatial depen...

This paper presents a novel dynamic network autoregressive conditional heteroscedasticity (ARCH) model based on spatiotemporal ARCH models to forecast volatility in the US stock market. To improve the forecasting accuracy, the model integrates temporally lagged volatility information and information from adjacent nodes, which may instantaneously sp...

The rapid advancement of models based on artificial intelligence demands innovative monitoring techniques which can operate in real time with low computational costs. In machine learning, especially if we consider artificial neural networks (ANNs), the models are often trained in a supervised manner. Consequently, the learned relationship between t...

Spatial and spatiotemporal volatility models are a class of models designed to capture spatial dependence in the volatility of spatial and spatiotemporal data. Spatial dependence in the volatility may arise due to spatial spillovers among locations; that is, in the case of positive spatial dependence, if two locations are in close proximity , they...

Motivated by empirical case studies and discussions of Bonas et al. (2024), this discussion paper critically examines challenges in the predictability of environmental processes, focusing on three key spheres: (a) predictability and interpretability, (b) predictability in dynamic environments, and (c) predictability into unknown spaces. These spher...

The assessment of corporate sustainability performance is extremely relevant in facilitating the transition to a green and low‐carbon intensity economy. However, companies located in different areas may be subject to different sustainability and environmental risks and policies. Henceforth, the main objective of this paper is to investigate the spa...

Extraction of plastic particles from soil is challenging and, thus, exceptionally little spatial information on plastic distribution at the field scale has been gathered. However, for environmental risk assessment, adequate sampling should complement coherent plastic profiling. In this study, we investigated the spatial distribution of mesoplastics...

In this paper, we study the problem of forecasting the next year's number of Atlantic hurricanes, which is relevant in many fields of applications such as land-use planning, hazard mitigation, reinsurance and long-term weather derivative market. Considering a set of well-known predictors, we compare the forecasting accuracy of both machine learning...

In this chapter, 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 spatial autoregressive fra...

We introduce a dynamic spatiotemporal volatility model that extends traditional approaches by incorporating spatial, temporal, and spatiotemporal spillover effects, along with volatility-specific observed and latent factors. The model offers a more general network interpretation, making it applicable for studying various types of network spillovers...

Stock market indices are volatile by nature, and sudden shocks are known to affect volatility patterns. The autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH) models neglect structural breaks triggered by sudden shocks that may lead to an overestimation of persistence, causing an upward bias in the estimates. Differen...

The assessment of corporate sustainability performance is extremely relevant in facilitating the transition to a green and low-carbon intensity economy. However, companies located in different areas may be subject to different sustainability and environmental risks and policies. Henceforth, the main objective of this paper is to investigate the spa...

This paper introduces a multivariate spatiotemporal autoregressive conditional heteroscedasticity (ARCH) model based on a vec-representation. The model includes instantaneous spatial autoregressive spill-over effects, as they are usually present in geo-referenced data. Furthermore, spatial and temporal cross-variable effects in the conditional vari...

Dear Colleagues, we are guest editing a Special Issue on "Network Monitoring with Machine Learning Methods" for the journal "Computers & Industrial Engineering" (Impact Factor: 7.9). The SI aims to provide a comprehensive platform for the dissemination of cutting-edge research, methodologies, and practical applications in the realm of machine learn...

Contemporary spatial statistics studies often underestimate the complexity of road networks, thereby inhibiting the strategic development of effective interventions for car accidents. In response to this limitation, the primary objective of this study is to enhance the spatiotemporal analysis of urban crash data. We introduce an innovative spatial-...

This study presents a comparative analysis of three predictive models with an increasing degree of flexibility: hidden dynamic geostatistical models (HDGM), generalised additive mixed models (GAMM), and the random forest spatiotemporal kriging models (RFSTK). These models are evaluated for their effectiveness in predicting PM2.5\documentclass[12pt]...

This paper presents a dynamic network autoregressive conditional heteroscedasticity (ARCH) model suitable for high-dimensional cases where multivariate ARCH models are typically no longer applicable. We adopt the theoretical foundations from spatiotemporal statistics and transfer the dynamic ARCH model processes to networks. The model integrates te...

Conventional modelling of networks evolving in time focuses on capturing variations in the network structure. However, the network might be static from the origin or experience only deterministic, regulated changes in its structure, providing either a physical infrastructure or a specified connection arrangement for some other processes. Thus, to d...

The popularity of urban micromobility has steadily grown in cities worldwide. There is a lack of comparative studies investigating factors influencing the travel behavior of shared micromobility in Europe. From this, we investigate shared bicycle, e-scooter, and e-moped usage in Berlin based on trip data from September 2019 to March 2022. We incorp...

This paper explores the estimation of a dynamic spatiotemporal autoregressive conditional het-eroscedasticity (ARCH) model. The log-volatility term in this model can depend on (i) the spatial lag of the log-squared outcome variable, (ii) the time-lag of the log-squared outcome variable, (iii) the spatiotemporal lag of the log-squared outcome variab...

A dynamic spatiotemporal stochastic volatility (SV) model is introduced, incorporating explicit terms accounting for spatial, temporal, and spatiotemporal spillover effects. Alongside these features, the model encompasses time-invariant site-specific factors, allowing for differentiation in volatility levels across locations. The statistical proper...

Geo-referenced data are characterised by an inherent spatial dependence due to geographical proximity. In this paper, we introduce a dynamic spatiotemporal autoregressive conditional heteroscedasticity (ARCH) process to describe the effects of (i) the log-squared time-lagged outcome variable, the temporal effect, (ii) the spatial lag of the log-squ...

This study presents a comparative analysis of three predictive models with an increasing degree of flexibility: hidden dynamic geostatistical models (HDGM), generalised additive mixed models (GAMM), and the random forest spatiotemporal kriging models (RFSTK). These models are evaluated for their effectiveness in predicting PM 2.5 concentrations in...

Spatial and spatiotemporal volatility models are a class of models designed to capture spatial dependence in the volatility of spatial and spatiotemporal data. Spatial dependence in the volatility may arise due to spatial spillovers among locations; that is, if two locations are in close proximity, they can exhibit similar volatilities. In this pap...

We explore the relationship between ammonia (NH 3) emissions and manure processed in the Lombardy region (Italy) at the sub-regional level (i.e., the agrarian subregions). We propose a two-step spatio-temporal statistical analysis. In the first step, we use several spatio-temporal specifications of area-level Small Area (SAE) Models to obtain credi...

Unlabelled: We propose a novel model selection algorithm based on a penalized maximum likelihood estimator (PMLE) for functional hidden dynamic geostatistical models (f-HDGM). These models employ a classic mixed-effect regression structure with embedded spatiotemporal dynamics to model georeferenced data observed in a functional domain. Thus, the...

The air in the Lombardy region, Italy, is one of the most polluted in Europe because of limited air circulation and high emission levels. There is a large scientific consensus that the agricultural sector has a significant impact on air quality. To support studies quantifying the role of the agricultural and livestock sectors on the Lombardy air qu...

Does an author’s name affect their chances of being cited? Here, Philipp Otto and Philipp Otto - yes, two researchers with the same name - investigate the impact of academic authorship characteristics on article citations

We present a model to estimate the technical requirements, including the photovoltaic area and battery capacity, along with the costs, for a four-person household to be 100% electrically self-sufficient in Germany. We model the hourly electricity consumption of private households with quasi-Fourier series and an autoregressive statistical model bas...

This article introduces a dynamic spatiotemporal stochastic volatility (SV) model with explicit terms for the spatial, temporal, and spatiotemporal spillover effects. Moreover, the model includes time-invariant site-specific constant log-volatility terms. Thus, this formulation allows to distinguish between spatial and temporal interactions, while...

The air in the Lombardy region, Italy, is one of the most polluted in Europe because of limited air circulation and high emission levels. There is a large scientific consensus that the agricultural sector has a significant impact on air quality. To support studies quantifying the role of the agricultural and livestock sectors on the Lombardy air qu...

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 i...

The rapid advancement of models based on artificial intelligence demands innovative monitoring techniques which can operate in real time with low computational costs. In machine learning, especially if we consider neural network (NN) learning algorithms, and in particular deep-learning architectures, the models are often trained in a supervised man...

We propose a novel model selection algorithm based on a penalized maximum likelihood estimator (PMLE) for functional hidden dynamic geostatistical models (f-HDGM). These models employ a classic mixed-effect regression structure with embedded spatiotemporal dynamics to model georeferenced data observed in a functional domain. Thus, the parameters of...

In spatial econometrics, we usually assume that the spatial dependence structure is known and that all information about it is contained in a spatial weights matrix W. However, in practice, the structure of W is unknown a priori and difficult to obtain, especially for asymmetric dependence. In this paper, we propose a data-driven method to obtain W...

Complex systems which can be represented in the form of static and dynamic graphs arise in different fields, e.g., communication, engineering and industry. One of the interesting problems in analysing dynamic network structures is monitoring changes in their development. Statistical learning, which encompasses both methods based on artificial intel...

This paper introduces a multivariate spatiotemporal autoregressive conditional heteroscedasticity (ARCH) model based on a vec-representation. The model includes instantaneous spatial autoregressive spill-over effects in the conditional variance, as they are usually present in spatial econometric applications. Furthermore, spatial and temporal cross...

Understanding the usage patterns for bike-sharing systems is essential in terms of supporting and enhancing operational planning for such schemes. Studies have demonstrated how factors such as weather conditions influence the number of bikes that should be available at bike-sharing stations at certain times during the day. However, the influences o...

Geo-referenced data are characterized by an inherent spatial dependence due to the geographical proximity. In this paper, we introduce a dynamic spatiotemporal autoregressive conditional heteroscedasticity (ARCH) process to describe the effects of (i) the log-squared time-lagged outcome variable, i.e., the temporal effect, (ii) the spatial lag of t...

Finding a suitable weight matrix in spatial GARCH models is a challenge when the actual locations are not known. Thus, we introduce an estimation procedure for spatial GARCH models when the locations are unknown. We suggest to use balance sheet data of companies as proxy for the spatial distance between companies. We provide a simulation study unde...

During the first wave of the COVID-19 pandemics in 2020, lockdown policies reduced human mobility in many countries globally. This significantly reduces car traffic-related emissions. In this paper, we consider the impact of the Italian restrictions (lockdown) on the air quality in the Lombardy Region. In particular, we consider public data on conc...

Spatial autoregressive models typically rely on the assumption that the spatial dependence structure is known in advance and is represented by a deterministic spatial weights matrix, although it is unknown in most empirical applications. Thus, we investigate the estimation of sparse spatial dependence structures for regular lattice data. In particu...

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 g...

Beach profile data sets provide valuable insight into the morphological evolution of sandy shorelines. However, beach monitoring schemes often show large variability in temporal and spatial intervals between beach profiles. Moreover, beach profiles are often incomplete (i.e. only a part of the profile is measured) and data gaps are unavoidable. The...

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...

With the growing availability of high-resolution spatial data, such as high-definition images, three-dimensional point clouds of light detection and ranging (LIDAR) scanners, or communication and sensor networks, it might become challenging to detect changes and simultaneously account for spatial interactions in a timely manner. To detect local cha...

In this paper, we provide some results on the class of spatial autoregressive conditional heteroscedasticity (ARCH) models, which have been introduced in recent literature to model spatial conditional heteroscedasticity. That means that the variance in some locations depends on the variance in neighboring locations. In contrast to the temporal ARCH...

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 statist...

In contrast to classical econometric approaches which are based on prespecified isotropic weighting schemes, we suggest that the spatial weighting matrix in the presence of directional dependencies should be estimated. We identify this direction based on different candidate neighbourhood sets. In this paper, we consider two different types of proce...

Understanding the usage patterns for bike-sharing systems is essential in terms of supporting and enhancing operational planning for such schemes. Studies have demonstrated how factors such as weather conditions influence the number of bikes that should be available at bike-sharing stations at certain times during the day. However, the influences o...

Complex systems which can be represented in the form of static and dynamic graphs arise in different fields, e.g. communication, engineering and industry. One of the interesting problems in analysing dynamic network structures is to monitor changes in their development. Statistical learning, which encompasses both methods based on artificial intell...

The application of network analysis has found great success in a wide variety of disciplines; however, the popularity of these approaches has revealed the difficulty in handling networks whose complexity scales rapidly. One of the main interests in network analysis is the online detection of anomalous behaviour. To overcome the curse of dimensional...

In this paper, we focus on tax competition among local governments and effects caused by agglomeration differentials between urban and rural municipalities. Due to the high number of competitors in regional tax competition, one would generally expect a `race to the bottom'. However, we observe high taxes in urban municipalities and moderate tax lev...

This paper investigates the effect of daily wind direction on the spatio-temporal distribution of particulate matter, PM2.5. Interdependencies between the PM2.5 values of different monitoring sites are characterized by incorporating time-varying anistropic spatial weighting matrices. These weights are parameterized with respect to wind direction, s...

The purpose of this paper is the statistical surveillance of spatial autoregressive models, where the observed process is monitored over both space and time. The considered spatial model contains disturbances with heavy tails. The control procedures based on exponential smoothing or cumulative sums are constructed using characteristic quantities in...

Spatial econometric research typically relies on the assumption that the spatial dependence structure is known in advance and is represented by a deterministic spatial weights matrix. Contrary to classical approaches, we investigate the estimation of sparse spatial dependence structures for regular lattice data. In particular, an adaptive least abs...

In this paper, the focus is on modeling local risks and uncertainties by generalized spatial autoregressive conditional heteroscedasticity (spGARCH) models. In contrast to temporal ARCH models, in which the distribution is known given the full information set of the prior periods, the distribution is not straightforward in spatial and spatiotempora...

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, Schmid, Garthoff (2016), we introduce an exponential spatial ARCH model in this paper. For this new model, maximum-likelihood estimat...

In time-series analyses and particularly in finance, generalised autoregressive conditional heteroscedasticity (GARCH) models are widely applied statistical tools for modelling volatility clusters (i.e. periods of increased or decreased risks). In contrast, the spatial dependence in conditional second moments of spatial and spatiotemporal processes...

This paper outlines a project on statistical modeling of coastal profiles. The objectives of the project are to evaluate on the morphological evolution of a coastline as well as to identify behavior of nourishments on different temporal and spatial scales. We propose the use of a flexible, spatiotemporal model for functional data, which can be esti...

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...

In this paper, we propose a two-step lasso estimation approach to estimate the full spatial weights matrix of spatiotemporal autoregressive models. In addition, we allow for an unknown number of structural breaks in the local means of each spatial locations. The proposed approach jointly estimates the spatial dependence, all structural breaks, and...

Jeske et al. (2018) give an overview on statistical methods for network surveillance. Many applications of network surveillance in various fields of science are presented. While sequential surveillance has been successfully applied in engineering for more than 80 years, the extension to newer fields like spatio-temporal processes, image analysis an...

Der vorliegende Beitrag befasst sich mit der statistischen Prozesskontrolle räumlicher autoregressiver Prozesse mit externen Regressoren. Das Ziel ist die Weiterentwicklung etablierter Methoden der zeitlichen Prozesskontrolle. Diese Ansätze werden für Anwendungen in der räumlichen Prozesskontrolle modifiziert. Wir illustrieren dieses Vorgehen anhan...

R-Package "spGARCH" for estimation of spatial and spatio-temporal ARCH models.

In this paper, we provide a spatiotemporal examination of German real-estate prices in 412 administrative districts. The price process is spatially autocorrelated and stationary over the considered period from 1995 to 2010. To quantify both spatial and temporal effects of the process, we apply different spatiotemporal models. These models are consi...

Aufgabe 2.1.1 Souvenir \({\circledast}\,{\circledast}\) Bradley bringt sich meist ein Tattoo als Souvenir aus dem Urlaub mit. Da er bei den meisten seiner Tätowierungen betrunken war, kann man davon ausgehen, dass er alle Tätowierer in seinem Umkreis mit gleicher Wahrscheinlichkeit aufsucht. Jetzt ist Bradley für zwei Wochen in Berlin. In seinem Um...

Aufgabe 1.1 Deutsches oder Holländisches Bier (a) $$\begin{aligned}\displaystyle\bar{x}&\displaystyle=\frac{1}{n}\sum_{i=1}^{n}x_{i}=\frac{1}{10}\cdot 85{,}7=8{,}570\\ \displaystyle\tilde{s}_{x}^{2}&\displaystyle=\frac{1}{n}\sum_{i=1}^{n}x_{i}^{2}-\bar{x}^{2}=\frac{1}{10}\cdot 871{,}49-8{,}57^{2}=13{,}7041\\ \displaystyle\tilde{s}_{x}&\displaystyle...

Aufgabe 3.1.1 Kinder pro Familie \({\circledast}\,{\circledast}\,{\circledast}\,{\circledast}\) Nehmen Sie an, die Anzahl der Kinder X in einer Familie folgt der Wahrscheinlichkeitsfunktion \(f_{\eta}\), welche für \(x\in\mathbb{N}\) als $$\begin{aligned}\displaystyle f_{\eta}(x)=\left\{\begin{array}[]{ll}\eta&x=0\\ \eta&x=1\\ 1-2\eta&x\geq 2\\ \en...

Aufgabe 4.1.1 Wartezeiten \({\circledast}\,{\circledast}\,{\circledast}\) Die Wartezeit W im Servicebetrieb „PiPi-Meißner“ bis zur Ankunft eines Kunden in Minuten sei Erlang-verteilt. Gegeben sei die dazugehörige Verteilungsfunktion F. $$F(w)=1-e^{-\lambda w}(1+\lambda w),\quad w\geq 0;\enspace\lambda\in\mathbb{R}$$ 1. Zeigen Sie, dass die zugehöri...

Aufgabe 1.1.1 Deutsches oder Holländisches Bier \(\circledast\) Die Hotelanlage „Beach-Fever“ auf einer beliebten spanischen Ferieninsel plant den Einkauf alkoholischer Getränke für die nächste Saison. Die Betreiber stehen vor der Entscheidung eine deutsche oder holländische Biersorte zu bestellen. Hierzu erfassten sie die Menge des getrunkenen Bie...