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Your data seems to have hierarchical structure and if so multilevel analysis would be appropriate. Some, like Nezlek, would say that if your data has hierarchical structure, than this is a sufficient reason why you should think of it in multilevel way and consider multilevel analysis. There are possibly three random effects: countries, companies and time, with possibly crossed structure (e.g. Raudenbush, 1993). Not accounting for the nested nature of the data could lead to biased results: if you want to infer about higher-levels based only on individual-level analysis, then the results could be biased (so called atomistic fallacy, check e.g. here).

However when choosing model for analysis you should also consider what is your aim and so it is not "yes" or "no" kind of choice and there could be other arguments for or against multilevel analysis.

Check a book by Gelman and Hill (2006) on mixed and hierarchical regression, it gives a nice and readable introduction on multilevel modelling plus tutorial on using lme4 and Bayesian estimation of this models.

Your data seems to have hierarchical structure and if so multilevel analysis would be appropriate. Some, like Nezlek, would say that if your data has hierarchical structure, than this is a sufficient reason why you should think of it in multilevel way and consider multilevel analysis. There are possibly three random effects: countries, companies and time, with possibly crossed structure (e.g. Raudenbush, 1993). Not accounting for the nested nature of the data could lead to biased results: if you want to infer about higher-levels based only on individual-level analysis, then the results could be biased (so called atomistic fallacy, check e.g. here).

However when choosing model for analysis you should also consider what is your aim and so it is not "yes" or "no" kind of choice and there could be other arguments for or against multilevel analysis.

Check a book by Gelman and Hill (2006) on mixed and hierarchical regression, it gives a nice and readable introduction on multilevel modelling plus tutorial on using lme4 and Bayesian estimation of this models.

Your data seems to have hierarchical structure and if so multilevel analysis would be appropriate. Some, like Nezlek, would say that if your data has hierarchical structure, than this is a sufficient reason why you should think of it in multilevel way and consider multilevel analysis. There are possibly three random effects: countries, companies and time, with possibly crossed structure (e.g. Raudenbush, 1993). Not accounting for the nested nature of the data could lead to biased results: if you want to infer about higher-levels based only on individual-level analysis, then the results could be biased (so called atomistic fallacy, check e.g. here).

However when choosing model for analysis you should also consider what is your aim and so it is not "yes" or "no" kind of choice and there could be other arguments for or against multilevel analysis.

Your data seems to have hierarchical structure and if so multilevel analysis would be appropriate. Some, like Nezlek, would say that if your data has hierarchical structure, than this is a sufficient reason why you should think of it in multilevel way and consider multilevel analysis. There are possibly three random effects: countries, companies and time, with possibly crossed structure (e.g. Raudenbush, 1993). Not accounting for the nested nature of the data could lead to biased results: if you want to infer about higher-levels based only on individual-level analysis, then the results could be biased (so called atomistic fallacy, check e.g. here).

However when choosing model for analysis you should also consider what is your aim and so it is not "yes" or "no" kind of choice and there could be other arguments for or against multilevel analysis.

Check a book by Gelman and Hill (2006) on mixed and hierarchical regression, it gives a nice and readable introduction on multilevel modelling plus tutorial on using lme4 and Bayesian estimation of this models.

Your data seems to have hierarchical structure and if so multilevel analysis would be appropriate. Some, like Nezlek, would say that if your data has hierarchical structure, than this is a sufficient reason why you should think of it in multilevel way and consider multilevel analysis. You can think of possibly three random effects: countries, companies and time, with possibly crossed structure (e.g. Raudenbush, 1993). Not accounting for the nested nature of the data could lead to biased results: if you want to infer about higher-levels based only on individual-level analysis, then the results could be biased (so called atomistic fallacy, check e.g. here).

However when choosing model for analysis you should also consider what is your aim and so it is not "yes" or "no" kind of choice and there could be other arguments for or against multilevel analysis.

Your data seems to have hierarchical structure and if so multilevel analysis would be appropriate. Some, like Nezlek, would say that if your data has hierarchical structure, than this is a sufficient reason why you should think of it in multilevel way and consider multilevel analysis. There are possibly three random effects: countries, companies and time, with possibly crossed structure (e.g. Raudenbush, 1993). Not accounting for the nested nature of the data could lead to biased results: if you want to infer about higher-levels based only on individual-level analysis, then the results could be biased (so called atomistic fallacy, check e.g. here).

However when choosing model for analysis you should also consider what is your aim and so it is not "yes" or "no" kind of choice and there could be other arguments for or against multilevel analysis.