Nonlinear Spatial Dynamic Panel Data Models with Endogenous Dominant Units
An Application to Share Data
Bibliographic Data
| ID | 19418266 |
|---|---|
| Authors | Jiajun Zhang (0000-0002-8859-9542, Shanghai Jiao Tong University), Chuanmin Zhao (0000-0003-2635-6307, Beijing Jiaotong University), Xi Qu (0000-0002-5747-3031, Shanghai Jiao Tong University, corresponding author) |
| Year | 2025 |
| Volume | 43 |
| Issue | 1 |
| Pages | 150-163 |
| Publication date | 2025-01-02 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2024.2329645 |
| OpenAlex | W4392641611 |
| Language | EN |
| Citations received | 1 |
| References cited | 70 |
This article develops a nonlinear spatial dynamic panel data model with one particularly interesting application to a structural interaction model for share data. To account for effects from dominant (popular) units, the spatial weights matrix in our model can allow for unbounded column sums. To account for heterogeneity, our model includes two-way fixed effects and heteroscedastic errors. We further consider the potential endogeneity of the spatial weight matrix constructed from socioeconomic distance. We investigate the quasi-maximum likelihood estimator (QMLE), generalized methods of moments estimator (GMME), and root estimator (RTE), and establish their consistency and asymptotic normality based on the near epoch dependence (NED) framework. The RTE can derive a relatively computationally simple and closed-form solution without evaluating the QMLE’s Jacobian matrix as well as the iterations by GMME. We consider both n,T→∞, and the strength of the dominant units is equal to 1 when T→∞. For the purpose of empirical analysis, we derive the marginal effects and their limiting distributions based on the proposed estimators. In an empirical application, we apply our model to China’s prefecture city-level data, revealing significant spillover effects of the tertiary industry share. These findings suggest that the development of the tertiary sector in large cities can foster its growth in small cities
Asymptotic distribution · Econometrics · Endogeneity · Estimator · Generalized method of moments · Heteroscedasticity · Nonlinear system · Panel data · Spatial dependence · Spatial econometrics · Statistics · Triangular array · Mathematics · Regional Economic and Spatial Analysis · Regional Economics and Spatial Analysis · Spatial and Panel Data Analysis · Applied Mathematics
Megaregions
Economic Growth of Nations
Introduction to Spatial Econometrics
Panel data methods for fractional response variables with an application to test pass rates
Estimation Methods for Models of Spatial Interaction
Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models
Global City-Regions
A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model
Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large
Estimation of spatial autoregressive panel data models with fixed effects
Large Sample Properties of Generalized Method of Moments Estimators
The effect of exports on labour share
Externalities from urban renewal
QML and Efficient GMM Estimation of Spatial Autoregressive Models with Dominant (Popular) Units
Recent Trends in Top Income Shares in the United States
The Productivity Advantages of Large Cities
Expansion and growth in Chinese cities, 1978–2010
Lost in space
Urban growth shadows
Urban and rural population growth in a spatial panel of municipalities
Productive Cities
Spatial Econometrics
| Citations per year | 0.5 |
|---|---|
| Citation span | 2024 - 2024 (1) |
| Highly cited | No |