arXiv 3 Apr 2023 · Statistics — Methodology · publishedStudies in Nonlinear Dynamics and Econometrics (2023) · 6 citations (OpenAlex)
arXiv:2304.01025 · PDF · DOI · OpenAlex · Extracted main text
Time series of counts are frequently analyzed using generalized integer-valued autoregressive models with conditional heteroskedasticity (INGARCH). These models employ response functions to map a vector of past observations and past conditional expectations to the conditional expectation of the present observation. In this paper, it is shown how INGARCH models can be combined with artificial neural network (ANN) response functions to obtain a class of nonlinear INGARCH models. The ANN framework allows for the interpretation of many existing INGARCH models as a degenerate version of a corresponding neural model. Details on maximum likelihood estimation, marginal effects and confidence intervals are given. The empirical analysis of time series of bounded and unbounded counts reveals that the neural INGARCH models are able to outperform reasonable degenerate competitor models in terms of the information loss.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chen H, Li Q, Zhu F (2020) Two classes of dynamic binomial integer-valued ARCH models | 0.928 | 4 | 3 | 100% |
| 2 | Wei CH, Zhu F, Hoshiyar A (2022) Softplus INGARCH models | 0.843 | 3 | 3 | 100% |
| 3 | Wei C.H., Jahn M (2022) Soft-clipping INGARCH models for time series of bounded counts | 0.843 | 3 | 3 | 100% |
| 4 | Ferland R, Latour A, Oraichi D (2006) Integer-valued GARCH processes | 0.737 | 3 | 2 | 100% |
| 5 | Möller T, Wei CH, Kim HY, Sirchenko A (2018) Modeling zero inflation in count data time series with bounded support | 0.693 | 6 | 1 | 100% |
| 6 | Dungey M, Martin V, Tang C, Tremayne A (2020) A threshold mixed count time series model: estimation and application | 0.693 | 5 | 1 | 100% |
| 7 | Sirchenko A (2020) A model for ordinal responses with heterogeneous status quo outcomes | 0.585 | 3 | 1 | 100% |
| 8 | Kock AB, Teräsvirta T (2014) Forecasting performances of three automated modelling techniques during the economic crisis 2007-2009 | 0.511 | 2 | 1 | 100% |
| 9 | Famoye F (1993) Restricted generalized Poisson regression model | 0.511 | 2 | 1 | 100% |
| 10 | Wei CH, Scherer L, Aleksandrov B, Feld M (2020) Checking Model Adequacy for Count Time Series by Using Pearson Residuals | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 17 scored citations.