{"id":{"repo_id":"passau-thes","oai_identifier":"oai:kobv.de-opus4-uni-passau:142"},"canonical_url":"https://search.dev.ndltd.org/etd/passau-thes/oai:kobv.de-opus4-uni-passau:142","repository":{"repo_id":"passau-thes","name":"Universität Passau","base_url":"https://opus4.kobv.de/opus4-uni-passau/oai"},"display":{"title":"Autoregressive Neural Network Processes - Univariate, Multivariate and Cointegrated Models with Application to the German Automobile Industry","abstract":"Prediction of economic variables is a basic component not only for economic models, but also for many business decisions. Nevertheless it is difficult to produce accurate predictions in times of economic crises, which cause nonlinear effects in the data. In this dissertation a nonlinear model for analysis of time series with nonlinear effects is introduced. Linear autoregressive processes are extended by neural networks to overcome the problem of nonlinearity. This idea is based on the universal approximation property of single hidden layer feedforward neural networks of Hornik (1993). Univariate Autoregressive Neural Network Processes (AR-NN) as well as Vector Autoregressive Neural Network Processes (VAR-NN) and Neural Network Vector Error Correction Models (NN-VEC) are introduced. Various methods for variable selection, parameter estimation and inference are discussed. AR-NN's as well as a NN-VEC are used for prediction and analysis of the relationships between 4 variables related to the German automobile industry: The US Dollar to Euro exchange rate, the industrial output of the German automobile industry, the sales of imported cars in the USA and an index of shares of German automobile manufacturing companies. Prediction results are compared to various linear and nonlinear univariate and multivariate models.","abstract_html":"Prediction of economic variables is a basic component not only for economic models, but also for many business decisions. Nevertheless it is difficult to produce accurate predictions in times of economic crises, which cause nonlinear effects in the data. In this dissertation a nonlinear model for analysis of time series with nonlinear effects is introduced. Linear autoregressive processes are extended by neural networks to overcome the problem of nonlinearity. This idea is based on the universal approximation property of single hidden layer feedforward neural networks of Hornik (1993). Univariate Autoregressive Neural Network Processes (AR-NN) as well as Vector Autoregressive Neural Network Processes (VAR-NN) and Neural Network Vector Error Correction Models (NN-VEC) are introduced. Various methods for variable selection, parameter estimation and inference are discussed. AR-NN's as well as a NN-VEC are used for prediction and analysis of the relationships between 4 variables related to the German automobile industry: The US Dollar to Euro exchange rate, the industrial output of the German automobile industry, the sales of imported cars in the USA and an index of shares of German automobile manufacturing companies. Prediction results are compared to various linear and nonlinear univariate and multivariate models.","abstract_has_math":false,"creators":["Dietz, Sebastian"],"institution":"Universität Passau","degree_name":null,"degree_level":"thesis.doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Moosmüller, Gertrud"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-18","date_published":"2011-05-18","updated_at":"2026-07-24T03:45:00Z","subjects":["Nonlinear Time Series Analysis","Neural Networks","Nonlinear Optimization","Econometrics"],"languages":[],"rights":["Standardbedingung laut Einverständniserklärung"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://opus4.kobv.de/opus4-uni-passau/frontdoor/index/index/docId/142","outbound_label":"Repository record","outbound_source":"source_url"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Moosmüller, Gertrud"]},{"key":"dc:creator","label":"Author","values":["Dietz, Sebastian"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:publisher","label":"Institution","values":["Universität Passau"]},{"key":"dc:type","label":"Dc Type","values":["doctoralThesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["thesis.doctoral"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Universität Passau"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Nonlinear Time Series Analysis","Neural Networks","Nonlinear Optimization","Econometrics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["Standardbedingung laut Einverständniserklärung"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Prediction of economic variables is a basic component not only for economic models, but also for many business decisions. Nevertheless it is difficult to produce accurate predictions in times of economic crises, which cause nonlinear effects in the data. In this dissertation a nonlinear model for analysis of time series with nonlinear effects is introduced. Linear autoregressive processes are extended by neural networks to overcome the problem of nonlinearity. This idea is based on the universal approximation property of single hidden layer feedforward neural networks of Hornik (1993). Univariate Autoregressive Neural Network Processes (AR-NN) as well as Vector Autoregressive Neural Network Processes (VAR-NN) and Neural Network Vector Error Correction Models (NN-VEC) are introduced. Various methods for variable selection, parameter estimation and inference are discussed. AR-NN's as well as a NN-VEC are used for prediction and analysis of the relationships between 4 variables related to the German automobile industry: The US Dollar to Euro exchange rate, the industrial output of the German automobile industry, the sales of imported cars in the USA and an index of shares of German automobile manufacturing companies. Prediction results are compared to various linear and nonlinear univariate and multivariate models."]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Autoregressive Neural Network Processes - Univariate, Multivariate and Cointegrated Models with Application to the German Automobile Industry","Autoregressive Neuronale Netze - Univariate, Multivariate und Kointegrierte Modelle mit einer Anwendung aus dem Bereich der deutschen Automobilindustrie"]}]}],"canonical_facts":{"dc:contributor":["Moosmüller, Gertrud"],"dc:creator":["Dietz, Sebastian"],"dc:description.abstract":["Prediction of economic variables is a basic component not only for economic models, but also for many business decisions. Nevertheless it is difficult to produce accurate predictions in times of economic crises, which cause nonlinear effects in the data. In this dissertation a nonlinear model for analysis of time series with nonlinear effects is introduced. Linear autoregressive processes are extended by neural networks to overcome the problem of nonlinearity. This idea is based on the universal approximation property of single hidden layer feedforward neural networks of Hornik (1993). Univariate Autoregressive Neural Network Processes (AR-NN) as well as Vector Autoregressive Neural Network Processes (VAR-NN) and Neural Network Vector Error Correction Models (NN-VEC) are introduced. Various methods for variable selection, parameter estimation and inference are discussed. AR-NN's as well as a NN-VEC are used for prediction and analysis of the relationships between 4 variables related to the German automobile industry: The US Dollar to Euro exchange rate, the industrial output of the German automobile industry, the sales of imported cars in the USA and an index of shares of German automobile manufacturing companies. Prediction results are compared to various linear and nonlinear univariate and multivariate models."],"dc:format.medium":["application/pdf"],"dc:publisher":["Universität Passau"],"dc:rights":["Standardbedingung laut Einverständniserklärung"],"dc:subject":["Nonlinear Time Series Analysis","Neural Networks","Nonlinear Optimization","Econometrics"],"dc:title":["Autoregressive Neural Network Processes - Univariate, Multivariate and Cointegrated Models with Application to the German Automobile Industry","Autoregressive Neuronale Netze - Univariate, Multivariate und Kointegrierte Modelle mit einer Anwendung aus dem Bereich der deutschen Automobilindustrie"],"dc:type":["doctoralThesis"],"thesis:degree_level":["thesis.doctoral"],"thesis:institution_name":["Universität Passau"]},"updated_at":"2026-07-24T03:45:00Z"}