There are some necessary pre- estimation works.You should check for stationarity and co-integration (if variables have unit root), and CSD (cross section dependence).

What can i do step by step, where should i start from?

Reza Moghaddasi

Check "Linear Mixed Models". You can refer to this paper

Article A comprehensive analysis of COVID-19 transmission and mortal...

Check this too

https://ourcodingclub.github.io/tutorials/mixed-models/

1. Check for possible unit root. Apply proper tests for panel data.

2. In the case of non-stationary data, examine existence of co-integration relationship.

3.Keep in mind that CSD test of Pesaran is of high importance as it affects outcome of unit root tests.

Before starting your estimation and thinking about what econometric methods to use, you should use your common sense to develope good specification. You may begin to think over, if and how variables could influence tourism and simply look at correlations (could also be via diagrams) one by one. In any case, I think, it is forebidden to relate absolute (monetary) amounts with indices in samples of units which are so different like countries. This is rather clear, if one looks at your estimated coefficients, which are almost unreadable.

Econometrics model of Data Envelopment Analysis

Anton Rainer my data looks like this, I simply want to study the impact of political instability and other governance indicators on tourism. But I can not begin the analysis not being sure that the numbers make sense. What do you advise me?

Dear Rand,

I could not open your data set, because I have no unpacking program. But that is not so important, as I have looked on some of the data in the World Bank Statistics.

My first proposal is to choose a higher dimension for the tourism revenues (e.g. billions of units - $?). This would lead to a better readability of the results.

For the first step of the specification of the model/equation to estimate, you need no data. Simply ask yourself and others, how the political stability and other political circumstances in a country would influence the decision to go there as a tourist and how much to spend.

Then I would look on the time series (from which one can get insights on changes over time) for some of the countries to find patterns between the exogenous variables and the dependent variable. The main problems may be: 1. The political variables might have a rather limited influence. 2. They may show too little variances. 3. A change of e.g. political stability will not lead to an instantaneous change of tourism. Improvement will likely have its positive influence only after some years, whereas a change to instability will have a shorter-run effect (asymmetric behaviour). 4. You will likely find high correlations between the political variables, which means that you should omit some of them. 5. The indices are far from an ideal cardinal scale, it is even not clear, whether a higher value really measures an improvement.

If you (hopefully) find-out some relations, you can try simple OLS estimations with one of the political variables lagged for several years and you may try to do that for transformed variables (percentage change of revenues with absolute differences of the indices).

The comparison between countries could, in principle, give you an impression of the political influence on (the level of) tourism, but I fear that other factors (landscape, seaside, history, culture) are by far dominating. If your aim is only to exercise econometric methods, you can try an estimate for the whole data set. For this purpose, I recommend, to calculate indices instead of the absolute revenue values (to neutralize the “non-political” differences between countries. But you should take into account that you have more explaining variables than you used in your first approach – namely all these political indicators with different lags. I think, that for such an approach you lose too many degrees of freedom and, therefore, you would not get reasonable results.

If I had this difficult (not to say: impossible) task, I would never do a pooled estimation, but I would try to find good time series estimates for all 10 countries, and then compare the estimated coefficients to find-out similarities.