Estimating spatial autoregressions under heteroskedasticity without searching for instruments

IF 3.5 2区 经济学 Q1 ECONOMICS Regional Science and Urban Economics Pub Date : 2024-04-25 DOI:10.1016/j.regsciurbeco.2024.104011
Yong Bao
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引用次数: 0

Abstract

This paper proposes estimating higher-order spatial autoregressions with spatial autoregressive errors and heteroskedastic error innovations without searching for instruments by explicitly exploiting the endogeneity of spatial lags in the outcome and error equations. The resulting estimator is shown to be consistent and asymptotically normal. Monte Carlo experiments demonstrate that it possesses better finite-sample properties than existing estimators. An empirical study of venture capital funding for biotechnology firms illustrates that spatial correlation stretches as far as 20 miles and that the number of venture capital firms in close proximity has stronger impact on the level of funding than as reported in an existing study.

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异方差条件下的空间自回归估计,无需寻找工具
本文提出通过明确利用结果方程和误差方程中空间滞后的内生性,在不寻找工具的情况下估计具有空间自回归误差和异方差误差创新的高阶空间自回归。结果表明,估计器具有一致性和渐近正态性。蒙特卡罗实验证明,与现有估计器相比,它具有更好的有限样本特性。对生物技术公司风险投资资金的实证研究表明,空间相关性最远可达 20 英里,与现有研究报告相比,邻近风险投资公司的数量对资金水平的影响更大。
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来源期刊
CiteScore
5.30
自引率
9.70%
发文量
63
期刊介绍: Regional Science and Urban Economics facilitates and encourages high-quality scholarship on important issues in regional and urban economics. It publishes significant contributions that are theoretical or empirical, positive or normative. It solicits original papers with a spatial dimension that can be of interest to economists. Empirical papers studying causal mechanisms are expected to propose a convincing identification strategy.
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