According to someone, using country-level data for study might bring the "aggregate fallacy" introduced by macro data, resulting in possible bias in estimate findings. If this is true, then why have researchers published several research articles in prestigious journals in which they analyzed country-level data? What if we conducted estimates using data from income groups? Simply said, if this is indeed a problem, what will be the solution?
Muhammad Ramzan , the point is heterogeneity, quality and availability of data. In countries like Brazil, ignoring the inequality may lead to a huge bias of omitted variables. It is a great mistake to ignore this heterogeneity, but many times the researchers deal with lack of good quality datasets. Deepening into states, or city level data is not easy. Our city level data is many times of poor quality, the municipalities do not have adequate staffs and cannot afford this adequate statistical service. If we go into state level data, or complex survey data (micro data), mainly accessible through our statistical office (say IBGE), with good techniques of panel estimation, we expect better results.
Adriano Marcos Rodrigues Figueiredo Thank you so much for your input, Professor. So you're suggesting aggregate fallacy is the same as heterogeneity? Sure, we address this by employing excellent panel estimate techniques such as the Driscoll-Kraay estimator, CS-ARDL, and many others.
Muhammad Ramzan , I believe this quote may express the meaning: the aggregation fallacy = aggregation bias, or better, the conclusion that what is true for the group must be true for the sub-group or individual. It's called aggregation bias because you're using aggregated data and extrapolating it inappropriately. It emerges, as one but not the only one reason, due to heterogeneity!
Adriano Marcos Rodrigues Figueiredo Professor, Great thanks. It's now perfectly clear.
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