| 来源类型 | Discussion paper
|
| 规范类型 | 论文
|
| 来源ID | DP11891
|
| DP11891 Structural breaks in panel data: Large number of panels and short length time series |
| Jan Hanousek
|
| 发表日期 | 2017-03-08
|
| 出版年 | 2017
|
| 语种 | 英语
|
| 摘要 | The detection of the (structural) break or so called change point problem has
drawn increasing attention from both theoretical and applied economic and financial
research over the last decade. A large part of the existing research concentrates on
the detection and asymptotic properties of the change point problem for panels with a
large time dimension T. In this article we study a different approach, i.e., we consider
the asymptotic properties with respect to N (number of panel members) while keeping
T fixed. This situation (N → ∞ but T being fixed and rather small) is typically related
to large (firm-level) data containing financial information about an immerse number of
firms/stocks across a limited number of years/quarters/months. We propose a general
approach for testing for the break(s) in this setup, which also allows their detection.
In particular, we show the asymptotic behavior of the test statistics, along with an
alternative wild bootstrap procedure that could be used to generate the critical values
of the test statistics. The theoretical approach is supplemented by numerous simulations
and extended by an empirical illustration. In the practical application we demonstrate
the testing procedure in the framework of the four factors CAPM model. In particular,
we estimate breaks in monthly returns of the US mutual funds during the period January
2006 to February 2010 which covers the subprime crises. |
| 主题 | Financial Economics
|
| 关键词 | Change point problem
Stationarity
Four factor capm model
Us mutual funds
Panel data
Bootstrap
|
| URL | https://cepr.org/publications/dp11891
|
| 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/540703
|
推荐引用方式
GB/T 7714 |
Jan Hanousek. DP11891 Structural breaks in panel data: Large number of panels and short length time series. 2017.
|
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
