Monte Carlo simulations as standard

Many of the estimation methods used in econometrics produce a biased result when applied to real datasets. In other words, if you want to know what value b is, the estimate tends to be b + some other constant value.

The theoretical understanding of the methods is imperfect, so we do not always know how much the bias is, and we therefore can't correct for it. Monte Carlo estimation gives a way of finding out without having perfect theoretical knowledge. It works by simulating lots of data from a model where you know the parameter values, then you apply the estimation method to the data and get estimated parameter values, then you subtract the real values from the estimated ones and you get your estimated bias. There is an assumption here (ergodicity, meaning convergence of sampled values to real ones) but it is not too restrictive.

It is unusual for papers to report corrections for their estimates based on Monte Carlo simulation, and much more usual to report estimates from different estimation methods. That's OK, but biases can be persistent across different methods, so it is not as comforting to get similar estimates as authors say.

So: here's to Monte Carlo reporting.