Project Details
Projekt
Model diagnostics for count time series
Subject Area
Statistics and Econometrics
Term
from 2020 to 2022
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 437270842
Time Series of counts arise in many different situations in economics and related fields. They can have various forms with respect to their dependence structure or their marginal distribution. As classical models for real-valued time series are not able to maintain the discrete nature of count data, a great many of models tailor-made for time series of counts have been developed. An adequate modelling of count data processes is important, to do forecasting, to monitor the subsequent process of the time series to reveal structural changes as soon as possible, or just to obtain a better understanding of the underlying count data process. The planned research project on model diagnostics for time series of counts comprises three central steps of model building: model identification, model selection and model validation.While a large number of methods have been proposed for real-valued (continuous) time series and are available since a long time, corresponding approaches for discrete-valued time series are far less developed. The existing methods are scarce and most of them are available only in rudimentary form (e.g., as heuristic application guidelines), and more rigorous, theory-based methods rely on restrictive model assumptions or focus on isolated characteristics of the process as, e.g., dispersion. Corresponding issues do also hold for goodness-of-fit tests: while numerous goodness-of-fit tests for continuous-valued time series have been proposed that are not only capable to test for specific models but also whole model classes, the applicability of available goodness-of-fit tests for time series of counts is restricted, e.g., to parametric assumptions.The planned research project features two complimentary lines of attack for model diagnosis in time series of counts. On the one hand, we aim to develop parametric methods for model diagnosis for time series of counts, which take into account various characteristics of the underlying distribution and/or dependence pattern. Further, diagnostic tools developed and widely applied for real-valued time series shall be made applicable also to time series of counts by using suitable parametric bootstrap implementations. On the other hand, we aim to develop goodness-of-fit tests based on joint distributions that are capable to consistently distinguish between different model classes. For the implementation, but also to allow for a broader applicability of the above-mentioned diagnostic tools, suitable semi-parametric bootstrap methods for time series of counts shall be developed and employed for model diagnostics. For the proposed methods, we want to investigate in detail the performance and the applicability by elaborate comparative simulations studies and applications to real data sets relevant in economic sciences.
DFG Programme
Research Grants
International Connection
Cyprus, Spain
Cooperation Partners
Professor Dr. Efstathios Paparoditis; Professor Dr. Pedro Pere Puig
