arXiv 22 Feb 2024 · Econometrics · 2 citations (OpenAlex)
arXiv:2402.14763 · PDF · DOI · OpenAlex · Extracted main text
This study introduces a novel spatial autoregressive model in which the dependent variable is a function that may exhibit functional autocorrelation with the outcome functions of nearby units. This model can be characterized as a simultaneous integral equation system, which, in general, does not necessarily have a unique solution. For this issue, we provide a simple condition on the magnitude of the spatial interaction to ensure the uniqueness in data realization. For estimation, to account for the endogeneity caused by the spatial interaction, we propose a regularized two-stage least squares estimator based on a basis approximation for the functional parameter. The asymptotic properties of the estimator including the consistency and asymptotic normality are investigated under certain conditions. Additionally, we propose a simple Wald-type test for detecting the presence of spatial effects. As an empirical illustration, we apply the proposed model and method to analyze age distributions in Japanese cities.
appendix boundary found by appendix_command · 45% 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 | Yang, H (2020) Random distributional response model based on spline method | 0.737 | 3 | 2 | 100% |
| 2 | Kelejian, H.H. and Prucha, I.R (2010) Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances | 0.644 | 2 | 2 | 100% |
| 3 | Hoshino, T (2022) Sieve IV estimation of cross-sectional interaction models with nonparametric endogenous effect self | 0.529 | 9 | 2 | 22% |
| 4 | Kress, R (2014) Linear Integral Equations, Third Edition | 0.511 | 2 | 1 | 100% |
| 5 | Yang, H., Baladandayuthapani, V., Rao, A.U., and Morris, J.S (2020) Quantile function on scalar regression analysis for distributional data | 0.511 | 2 | 1 | 100% |
| 6 | Bigot, J., Gouet, R., Klein, T., and López, A (2017) Geodesic pca in the wasserstein space by convex pca | 0.405 | 1 | 1 | 100% |
| 7 | Ando, T., Li, K., and Lu, L (2023) A spatial panel quantile model with unobserved heterogeneity | 0.405 | 1 | 1 | 100% |
| 8 | Belloni, A., Chernozhukov, V., Chetverikov, D., and Kato, K (2015) Some new asymptotic theory for least squares series: Pointwise and uniform results | 0.405 | 1 | 1 | 100% |
| 9 | Blundell, R., Chen, X., and Kristensen, D (2007) Semi-nonparametric iv estimation of shape-invariant Engel curves | 0.405 | 1 | 1 | 100% |
| 10 | Breunig, C., Mammen, E., and Simoni, A (2020) Ill-posed estimation in high-dimensional models with instrumental variables | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 35 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Network Autoregressive Models for Functional Panel Data | 0.737 | 3 | 2 |
| 2 | IV regression with distribution-valued outcomes | 0.405 | 1 | 1 |