A semiparametric model for heterogeneous panel data with fixed effects
具有固定效应的异质面板半参数模型AI 解读 ↓
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具有固定效应的异质面板半参数模型
- 研究问题
- 在横截面和时间维度都较大的面板中，如何估计异质的非参数协变量效应，同时容纳与协变量相关的个体和时间效应？
- 方法与思路
- 作者以共同成分结构降低异质协变量效应的维度，推导估计量收敛率和渐近分布，并应用于交易场所分散化与市场质量。
- 材料陈述的贡献
- 摘要称，方法允许异质非参数效应与相关固定效应；应用发现分散化效应非线性且非单调，模型隐含完全竞争下市场质量更高。
- 阅读关注点
- 应核对共同成分的识别、维度限制及双大样本条件，并查看交易分散化的内生性处理和竞争结构比较所依赖的支持范围。
- 阅读边界
- 所给出版社摘要未列出因果识别条件与效应数值；不能据此确认分散化的因果影响，或竞争结构外推比较的有效性。
原文摘要
This paper develops methodology for semiparametric panel data models in a setting where both the time series and the cross section are large. Such settings are common in finance and other areas of economics. Our model allows for heterogeneous nonparametric covariate effects as well as unobserved time and individual specific effects that may depend on the covariates in an arbitrary way. To model the covariate effects parsimoniously, we impose a dimensionality reducing common component structure on them. In the theoretical part of the paper, we derive the asymptotic theory for the proposed procedure. In particular, we provide the convergence rates and the asymptotic distribution of our estimators. In the empirical part, we apply our methodology to a specific application that has been the subject of recent policy interest, that is, the effect of trading venue fragmentation on market quality. We use a unique dataset that reports the location and volume of trading on the FTSE 100 and FTSE 250 companies from 2008 to 2011 at the weekly frequency. We find that the effect of fragmentation on market quality is nonlinear and non-monotonic. The implied quality of the market under perfect competition is superior to that under monopoly provision, but the transition between the two is complicated.