| 来源类型 | Discussion paper
|
| 规范类型 | 论文
|
| 来源ID | DP15246
|
| DP15246 Understanding Persistence |
| Morgan Kelly
|
| 发表日期 | 2020-09-03
|
| 出版年 | 2020
|
| 语种 | 英语
|
| 摘要 | A large literature on persistence finds that many modern outcomes strongly reflect characteristics of the same places in the distant past. These studies typically combine unusually high t statistics with severe spatial autocorrelation in residuals, suggesting that some findings may be artefacts of underestimating standard errors or of fitting spatial trends. For 25 studies in leading journals, I apply three basic robustness checks against spatial trends and find that effect sizes typically fall by over half, leaving most well known results insignificant at conventional levels.
Turning to standard errors, there is currently no data-driven method for selecting an appropriate HAC spatial kernel. The paper proposes a simple procedure where a kernel with a highly flexible functional form is estimated by maximum likelihood. After correction, standard errors tend to rise substantially for cross sectional studies but to fall for panels. Overall, credible identification strategies tend to perform no better than naive regressions. Although the focus here is on historical persistence, the methods apply to regressions using spatial data more generally. |
| 主题 | Economic History
|
| URL | https://cepr.org/publications/dp15246
|
| 来源智库 | Centre for Economic Policy Research (United Kingdom)
|
| 资源类型 | 智库出版物
|
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/544221
|
推荐引用方式
GB/T 7714 |
Morgan Kelly. DP15246 Understanding Persistence. 2020.
|
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