Which estimation technique minimizes the MAPE?

Suppose we have two estimation techniques:

• Linear Least Squares, which aims to minimize squared residuals
• Least Absolute Deviation, which aims to minimize absolute residuals

We have a model, which purpose is prediction. More specifically, we want the model to perform good based on the prediction evaluation measure: Mean Absolute Prediction Error (MAPE). Assuming we have no information of the data, can we say something about which estimation technique will likely perform better?

You may be better off using a custom optimization routine that directly attempts to minimize the MAPE. The problem being, of course, that the MAPE is not differentiable at perfect forecasts. Alternatively, you could try to estimate full predictive densities and then output the (-1)-median of this density as a point forecast, which is the functional that minimizes the MAPE in expectation (Gneiting, 2011, p. 752 with $$\beta=-1$$).