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AI 根据所提供的论文材料归类:摘要明确采用跨国面板研究技术扩散,并以共同未观测因子解释强截面相关,因此归入宏观面板与因子模型。

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关注这篇论文的作者
Cem Ertur
Antonio Musolesi

弱与强截面相关:国际技术扩散的面板数据分析

研究问题
跨国技术知识溢出模型中的弱、强截面相关如何影响设定与估计,忽略强相关是否会扭曲研发、贸易溢出和教育效应的判断?
方法与思路
作者先估计截面相关的指数,随后采用共同相关效应方法处理因子结构,并将估计结果与仅允许弱相关的空间模型比较。
材料陈述的贡献
摘要报告强截面相关证据,并称富国更受益于国内研发和地理溢出、小国更受益于贸易溢出;处理因子后教育数量效应不显著。
阅读关注点
阅读全文时应核对截面相关指数的判别依据、共同相关效应的设定与有效条件、非平稳因子的处理,以及国家异质性结果的稳健性。
阅读边界
摘要未说明样本范围、变量构造、具体估计式和识别条件,无法据此将研发与溢出系数解释为已确立的因果效应。

原文摘要

Summary This paper provides an econometric examination of technological knowledge spillovers among countries by focusing on the issue of error cross‐sectional dependence, particularly on the different ways—weak and strong—that this dependence may affect model specification and estimation. A preliminary analysis based on estimation of the exponent of cross‐sectional dependence provides a clear result in favor of strong cross‐sectional dependence. This result has relevant implications in terms of econometric modeling and suggests that a factor structure is preferable to a spatial error model. The common correlated effects approach is then used because it remains valid in a variety of situations that are likely to occur, such as the presence of both forms of dependence or the existence of nonstationary factors. According to the estimation results, richer countries benefit more from domestic R&D and geographic spillovers than poorer countries, while smaller countries benefit more from spillovers originating from trade. The results also suggest that when the problem of (possibly many) correlated unobserved factors is addressed the quantity of education no longer has a significant effect. Finally, a comparison of the results with those obtained from a spatial model provides interesting insights into the bias that may arise when we allow only for weak dependence, despite the presence of strong dependence in the data. Copyright © 2016 John Wiley & Sons, Ltd.

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