[cengiz hokka]

thank you very much dear Clyde Schechter for reply to me all questions.

[cengiz hokka]

Excuse me Mr Clyde Schechter, I did not quite understand the following part of your last statement:

The analysis may be finished if you do not, to answer your research questions, need to separate out within-level from between-level effects.

May I ask what you mean by separating out within-level effects from between-level effects here?

By the way, in either case, nested or crossed, there would not be a year level unless you have multiple observations of the same bank and country within a year.

And finally, what you mean here is that if there are no multiple observations of the same bank and country in a year (for example, 2011), the year effect cannot be looked at, in other words, there is no year dimension?

[Clyde Schechter]

May I ask what you mean by separating out within-level effects from between-level effects here?

When you have multilevel data, the effect of differences in effects on outcome of an explanatory variable in observations within the same higher-level unit may be different from the effects of the same variable in observations between different higher-level units. Sometimes this is important, and when your research goals refer to the effects of some variable on some outcome, you need to be clear whether you are talking about the effects of within-higher-level-unit differences or the effects between different higher-level units. For example, there may be differences in some outcome between married people and unmarried people. But this effect of marital status between people may not be the same as the effect on a person of getting married or getting divorced/widowed. Here's a toy example that shows clearly how this can happen in data:

clear
set obs 5
gen panel_id = _n
expand 2

set seed 1234
by panel_id , sort: gen y = 4*panel_id - _n + 3 + rnormal(0, 0.5)
by panel_id: gen x = panel_id + _n

xtset panel_id

xtreg y x, fe
regress y x

//    GRAPH THE DATA TO SHOW WHAT'S HAPPENING
separate y, by(panel_id)

graph twoway connect y? x || lfit y x

But sometimes there is no difference, or no difference large enough to matter, between within and between unit effects. So you need to think about the real world data generating process to consider whether there is likely to be a difference in your situation. If there is, then you also need to decide which effect is relevant to your research question, and run a transformed model (as discussed a few posts back in this thread) that separates them out. Basically, in the transformed model, the group mean variables give you the between-unit effects, and the deviation from group mean variables give you the within-unit effects.

And finally, what you mean here is that if there are no multiple observations of the same bank and country in a year (for example, 2011), the year effect cannot be looked at, in other words, there is no year dimension?

No, that's not what I mean. When you have longitudinal data, there is clearly a time dimension in the model. But if each unit has only one observation per year, you cannot have a separate time level in the model. When you have a multi-level model, each level is associated with (at least) a random intercept. If you were to write out the model equation it might look like y = b0 + b1x1 + ... + u_country + u_bank + epsilon. Here, u_country and u_bank refer to the random intercepts at those respective levels. And epsilon is the bottom-level residual. What would happen if you tried to add a u_year to the model. Because there is only one observation per bank, there would be no way to separate the variation at that level into two separate components: u_year (a part that is constant across all observations in the year) and epsilon (the part that is idiosyncratic to the specific observation). Any partition of the residual variation after u_country and u_bank would be equivalent to any other. So the model is unidentifiable. Usually the result is that if you attempt to fit such a model, it won't converge. But even if it did converge, the distinction between u_year and epsilon (residual) would be meaningless and those results would be uninterpretable.

[cengiz hokka]

thank you so much for rely to all my post,
best