arXiv 19 Feb 2025 · Statistics — Methodology
arXiv:2502.13431 · PDF · DOI · OpenAlex · Extracted main text
This study proposes a novel functional vector autoregressive framework for analyzing network interactions of functional outcomes in panel data settings. In this framework, an individual's outcome function is influenced by the outcomes of others through a simultaneous equation system. To estimate the functional parameters of interest, we need to address the endogeneity issue arising from these simultaneous interactions among outcome functions. This issue is carefully handled by developing a novel functional moment-based estimator. We establish the consistency, convergence rate, and pointwise asymptotic normality of the proposed estimator. Additionally, we discuss the estimation of marginal effects and impulse response analysis. As an empirical illustration, we analyze the demand for a bike-sharing service in the U.S. The results reveal statistically significant spatial interactions in bike availability across stations, with interaction patterns varying over the time of day.
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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 | Zhu, Xuening and Cai, Zhanrui and Ma, Yanyuan (2022) Network functional varying coefficient model | 1.000 | 6 | 3 | 100% |
| 2 | Lin, Xu and Lee, Lung-Fei (2010) GMM estimation of spatial autoregressive models with unknown heteroskedasticity | 0.737 | 3 | 3 | 67% |
| 3 | Hoshino, Tadao (2024) Functional Spatial Autoregressive Models self | 0.737 | 3 | 2 | 100% |
| 4 | Eren, Ezgi and Uz, Volkan Emre (2020) A review on bike-sharing: The factors affecting bike-sharing demand | 0.644 | 2 | 2 | 100% |
| 5 | Lee, Lung-Fei and Yu, Jihai (2010) Estimation of spatial autoregressive panel data models with fixed effects | 0.644 | 2 | 2 | 100% |
| 6 | Yang, Zixin and Song, Xiaojun and Yu, Jihai (2025) Estimation of spatial autoregressive panel data models with nonparametric endogenous effect | 0.644 | 2 | 2 | 100% |
| 7 | Rainer Kress (2014) Linear Integral Equations, Third Edition | 0.511 | 2 | 2 | 50% |
| 8 | Hoshino, Tadao (2022) Sieve IV estimation of cross-sectional interaction models with nonparametric endogenous effect self | 0.511 | 2 | 2 | 50% |
| 9 | Jenish, Nazgul and Prucha, Ingmar R (2012) On spatial processes and asymptotic inference under near-epoch dependence | 0.511 | 2 | 2 | 50% |
| 10 | Lee, Lung-Fei and Yu, Jihai (2014) Efficient GMM estimation of spatial dynamic panel data models with fixed effects | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 48 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Quantile Vector Autoregression without Crossing | 0.405 | 1 | 1 |