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Dynamic Stochastic General Equilibrium models made (relatively) easy with R

By Peter’s stats stuff – R

General Equilibrium economic models

To expand my economics toolkit I’ve been trying to get my head around Computable General Equilibrium (CGE) and Dynamic Stochastic General Equilibrium (DSGE) models. Both classes of model are used in theoretical and policy settings to understand the impact of changes to an economic system on its equilibrium state.

I’m not a specialist in this area so the below should be taken as the best effort by a keen amateur. Corrections or suggestions welcomed!

CGE models have the simpler approach of the two and a longer history and have been very widely applied to practical policy questions such as the impact of trade deals. Many economic consultancies have their own in-house CGE model/s which they wheel out and aadapt to a range of their clients’ questions. They work by comparing static equilibrium states, assumed to meet requirements (such as markets clearing effectively instantly) needed to be in equilibrium, “calibrated” to the real economy by choosing a set of numbers for the various parameters that match the state of the economy at a particular point in time. The model is then adjusted – for example, to allow for changes in prices from a free trade agreement – and the new equilibrium compared to the old.

DSGE models are also based on an assumption of a steady state equilibrium of the economy, but they allow for real amounts of time being taken to move towards that steady state, and for a random (ie stochastic) element in the path taken towards that steady state. This greatly improves their coherence in terms of philosophy of science – compared to a CGE which simply calibrates to a single point of time and doesn’t have any degrees of freedom to quantify uncertainty or the fit of the model to reality, …read more

Source:: r-bloggers.com


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