I'm a statistician/machine learning scientist more familiar with molecular bio than economics. Trying to find out if an issue I perceive in bio modeling also occurs in econometric modeling.

A common task in computational biology is to build a model of some biochemical process from data. These biochemical processes are always dynamic, i.e. something you might model with an ODE or Kalman filter. However, there is a bias towards modeling such a process from equilibrium data. This means, you wait until the process has reached some equilibrium (homeostasis), and you take measurements across subjects/replicates. This is because for many reasons it is much easier and cheaper to acquire equilibrium data relative to time series data. Training a model only on equilibrium data is a problem of course if you want the model to be predictive when the system is not at equilibrium.

I wonder if the same bias happens with econometric models? Clearly, in terms of statistical methodology, cross-sectional data is typically cheaper and easier to acquire than time-series data. And I know economists are concerned with modeling equilibria. I'm hoping there is a place someone can point me to that talks about this issue in the econometric context, perhaps with an example?


Economists (most of them) build their models assuming most of the time stochastic dynamic equilibrium. So Economics does not contrast "dynamic" with "equilibrium" - it synthesizes them.

It is stochastic in the sense that random shocks are acknowledged. It is dynamic in the sense that it may revolve around a deterministic or stochastic trend. And it is an equilibrium because, exactly, it is assumed "chained" -not necessarily to some level but at least to some trend.

The "bias induced" here, the price to pay, is that mainstream economic models are not very good at predicting derailment and crises. On this, perhaps it would be useful to read this column article by well-known economist Robert Lucas.


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