### autocorrelation problem in within-between random effect model

Posted:

**Fri Jun 07, 2019 3:19 pm**Hi. I have a trouble about serial correlation problem in multilevel within-between RE model. I've been trying to analyze a relationship between the level of corruption (I use Corruption Perception Index, which is a continuous variable between 0 and 100) and the independence of a governmental institution specialized in corruption crime in each country (the value takes somewhere between 0 and 1, and I take it as a continuous variable) with several controls. The data I am dealing with is a panel data with maximum 23 years (but 14 years on average) and 46 countries.

First of all, Hausman test indicates that there is a systematic difference between within- and between-effect, which implies FE model would be a better choice than RE model. However, considering that the independent variable of my interest (independence of an governmental institution) is hardly time variant, employment of FE model would result in losing a lot of information and over interpretation. Hence I would like to use multilevel model (within-between RE).

However, a problem arises when taking into account that the level of corruption has a pretty high serial correlation. As inclusion of lagged dependent variables in RE model is problematic, I wonder how I can solve this serial correlation problem. Some people say clustering (robust) standard error would solve the serial correlation problem especially with a large N small t panel data (which is my case). Could this argument be applied to multilevel model (within-between RE) and "runmlwin" command? If not, is there any way I can solve the autocorreleation problem?

Many thanks,

Umito

First of all, Hausman test indicates that there is a systematic difference between within- and between-effect, which implies FE model would be a better choice than RE model. However, considering that the independent variable of my interest (independence of an governmental institution) is hardly time variant, employment of FE model would result in losing a lot of information and over interpretation. Hence I would like to use multilevel model (within-between RE).

However, a problem arises when taking into account that the level of corruption has a pretty high serial correlation. As inclusion of lagged dependent variables in RE model is problematic, I wonder how I can solve this serial correlation problem. Some people say clustering (robust) standard error would solve the serial correlation problem especially with a large N small t panel data (which is my case). Could this argument be applied to multilevel model (within-between RE) and "runmlwin" command? If not, is there any way I can solve the autocorreleation problem?

Many thanks,

Umito