I have ever read which mentioned that serial-correlation (or autocorrelation) in panel data, especially when the number of N is large and T is small, may ignore.
there are some reasons:
1. in panel data, ordering the data may affect the autocorrelation value. Panel data open the possibility of reordering in many ways, which affected the value of autocorrelation. But in time series data, the value of serial correlation is unique since we can't reorder data in many ways.
2. when the N is large and T is small, this data strongly faces heteroscedasticity than serial correlation. So the serial correlation is a minor problem.
Perhaps anybody can give me insight more into this issue? On how actually to deal with autocorrelation issue in panel data.
No, you cannot. It affects the precision of estimates (standard errors). If your N is large, I advise using clustered standard errors at the level of the units (N). In this way, your standard errots are robuts to any sort of heteroskedasticity or serial correlation.
I hope it helps.
See these links:
https://www.statalist.org/forums/forum/general-stata-discussion/general/1381813-does-anyone-care-about-serial-correlation-in-panels
https://www.researchgate.net/post/Solving-cross-sectional-dependence-and-serial-correlation-in-panel-data
Serial correlation can be a very serious problem for inference. Even when N is large and T is small. A classic example of this is serial correlation in difference-in-differences analysis, as pointed out in Bertrand, Duflo and Mullainathan (2003):
https://www.nber.org/papers/w8841
many thanks for all of your advices @José-Ignacio Antón, @Remal Al-Gounmeein, @Jesse A Matheson. It really helpful for me.
Dear Muhammad.
No, you cannot ignore serial correlation because it's very important in panel analysis.
Regards
Hi Muhamad! Good research practice is not to ignore such issues in the data, especially since there are adequate methods to deal with these problems. Here are a few ideas to keep in mind:
1. We usually suspect an autocorrelation problem by conceptually analyzing the problem and descriptively analyzing the data. However, the first step is to use a statistical test to determine whether or not the problem exists. If the test indicates no autocorrelation, then it is not a concern for the model.
- To test for the presence of serial correlation, you can use the Arellano–Bond test for first- and second-order autocorrelation in the first-differenced errors. See https://doi.org/10.1177%2F1536867X1801800106 and https://www.stata.com/manuals/xt.pdf (p. 28 onwards)
2. If a test proves that autocorrelation exists, then a specific method must be used to deal with the problem. A good alternative is to implement Fit panel-data models by using GLS. This approach allows several specification options; for example, it specifies a heteroskedastic error structure with no cross-sectional correlation, specifies a heteroskedastic error structure with cross-sectional correlation, and specifies the assumed autocorrelation within panels. See https://www.stata.com/manuals/xt.pdf (p. 186 onwards).
These considerations are quite general; it is necessary to consider the distribution of the data to find the best ways of working. In addition, the research question is also important in determining which methods are best suited to state the structure of the problem.
I hope this information is useful.
Best regards!
Let's connect! www.linkedin.com/in/maría-josé-ibáñez
- Badges
- Science topic
- Similar topics
- Correction