We tend to get interested in large developed economies because this is where we live and where loads of data are available to make interesting observations and run empirical exercises. But there are also lots of issues elsewhere that need to be studied and that are relevant, at least locally. Those are harder, because data is scarce and also because theory that could guide us may not be available.
Take as an example a study by Kenji Moriyama and Abdul Naseer that tries to forecast inflation in Sudan. This is very important in an economy as disrupted as this one, because the lack of efficient financial markets and banking leaves only currency as a tool for savings. If inflation is high or uncertain, using it for savings is not likely either. The problem is that there is very little data available for Sudan. The authors use ARMA techniques, which at least rely on a limited number of series but require rather long samples. But they can rely only on eight years of monthly data.
Another way to work this out would be to have a structural model, but this requires quite a few additional data series, which are not likely to be available. Maybe then, letting theory guide us could be a solution. This is particularly important in a country where shocks are very important and regime changes are likely. The Lucas Critique has a lot of bite here, and you want to go as deep as possible in the structure in order to study the reactions of agents and markets to situations that may never have happened before. But do we really have a good theory to describe Sudan, with its civil war, population displacement, humantarian aid, etc.? This is the kind of theory that needs development, as this is where the marginal return of theory to real-world well-being is the highest. And this is not just about inflation in Sudan.