论文标题
空间面板数据模型的鞍点近似值
Saddlepoint approximations for spatial panel data models
论文作者
论文摘要
在空间面板数据模型中，我们为高斯最大似然估计器开发了新的高阶渐近技术，具有固定效果，时变的协变量和空间相关的误差。我们的鞍点密度和尾部面积近似特征$ o（1/（n（t-1）））$的相对误差为横截面维度，而$ n $是时间序列尺寸。主要的理论工具是在非相同分布的设置中的倾斜边缘技术。密度近似始终是非负的，不需要重新采样，并且在尾部中是准确的。在存在滋扰参数的情况下，蒙特卡洛对密度近似和测试的实验说明了我们在一阶渐近学和Edgeworth膨胀上的近似性能良好。在经合组织（经济合作与发展组织）国家（基于一阶渐近技术和鞍座技术）之间的分歧，对经合组织（经济合作与发展组织）国家的投资关系的经验应用表明。
We develop new higher-order asymptotic techniques for the Gaussian maximum likelihood estimator in a spatial panel data model, with fixed effects, time-varying covariates, and spatially correlated errors. Our saddlepoint density and tail area approximation feature relative error of order $O(1/(n(T-1)))$ with $n$ being the cross-sectional dimension and $T$ the time-series dimension. The main theoretical tool is the tilted-Edgeworth technique in a non-identically distributed setting. The density approximation is always non-negative, does not need resampling, and is accurate in the tails. Monte Carlo experiments on density approximation and testing in the presence of nuisance parameters illustrate the good performance of our approximation over first-order asymptotics and Edgeworth expansions. An empirical application to the investment-saving relationship in OECD (Organisation for Economic Co-operation and Development) countries shows disagreement between testing results based on first-order asymptotics and saddlepoint techniques.
