Hi there,
I am having a problem with a dataset I am using for my thesis on Stata 18. I am using the Chapel Hill Expert Survey (CHES) dataset with parties nested in countries and two time points: 2017 and 2019.
It is my first time using panel data and, more generally, carrying out multi-level modelling. I am interested to run fixed effect models, yet I get stuck at the beginning because of the following error:
The issue relates to the fact that each country variable is associated to multiple (different) observations (in this case, parties or better party_id). In short, country is not a unique identifier for my observations.
Nonetheless, country is my only second level predictor and I would like to take into account the clustering effect present in the data.
Here is a snapshot of my dataset:
Suggestions would be highly appreciated.
Thanks in advance
Mattia
I am having a problem with a dataset I am using for my thesis on Stata 18. I am using the Chapel Hill Expert Survey (CHES) dataset with parties nested in countries and two time points: 2017 and 2019.
It is my first time using panel data and, more generally, carrying out multi-level modelling. I am interested to run fixed effect models, yet I get stuck at the beginning because of the following error:
Code:
xtset country year, yearly repeated time values within panel r(451);
The issue relates to the fact that each country variable is associated to multiple (different) observations (in this case, parties or better party_id). In short, country is not a unique identifier for my observations.
Nonetheless, country is my only second level predictor and I would like to take into account the clustering effect present in the data.
Here is a snapshot of my dataset:
Code:
* Example generated by -dataex-. For more info, type help dataex clear input double(country year party_id) 3 2019 301 3 2017 301 3 2017 302 3 2019 302 3 2019 303 3 2017 303 3 2019 304 3 2017 304 3 2019 306 3 2017 306 3 2019 308 3 2017 308 3 2019 310 3 2017 310 3 2019 311 3 2019 312 3 2017 350 3 2017 351 4 2019 401 4 2017 401 4 2017 402 4 2019 402 4 2017 403 4 2019 403 4 2017 404 4 2019 404 4 2017 412 4 2017 413 4 2017 414 4 2019 415 4 2017 415 4 2019 416 4 2019 417 4 2019 418 4 2017 450 5 2019 501 5 2017 501 5 2017 502 5 2019 502 5 2019 504 5 2017 504 5 2019 506 5 2017 506 5 2017 507 5 2017 511 5 2019 511 5 2019 513 5 2017 517 5 2019 517 5 2019 524 5 2017 525 5 2019 525 5 2017 526 5 2019 526 5 2019 527 5 2019 528 5 2017 550 5 2019 550 end
Suggestions would be highly appreciated.
Thanks in advance
Mattia
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