DSGE

Dynamic stochastic general equilibrium modeling (abbreviated DSGE or sometimes SDGE or DGE) is a branch of applied general equilibrium theory that is influential in contemporary macroeconomics. The DSGE methodology attempts to explain aggregate economic phenomena, such as economic growth, business cycles, and the effects of monetary and fiscal policy, on the basis of macroeconomic models derived from microeconomic principles.

While traditional macroeconometric forecasting models are vulnerable to the Lucas critique—that claims that the effects of an economic policy cannot be predicted using historical data from a period when that policy was not in place—microfounded models should not be, at least in theory. Further, since the microfoundations are based on the preferences of the decision-makers in the model, DSGE models feature a natural benchmark for evaluating the welfare effects of policy changes.

Furthermore, as their name indicates, DSGE models are dynamic, studying how the economy evolves over time. They are also , taking into account the fact that the economy is affected by random shocks such as technological change, fluctuations in the price of oil, or changes in macroeconomic policy-making. This contrasts with the static models studied in Walrasian general equilibrium theory, applied general equilibrium models and some computable general equilibrium models.

For a coherent description of the macroeconomy, DSGE models must spell out the following economic 'ingredients'.

Traditional macroeconometric forecasting models used by central banks in the 1970s, and even today, estimated the dynamic correlations between prices and quantities in different sectors of the economy, and often included thousands of variables. Since DSGE models start from microeconomic principles of constrained decision-making, instead of just taking as given observed correlations, they are technically more difficult to solve and analyze. Therefore, they usually abstract from so many sectoral details, and include far fewer variables: just a few variables in theoretical DSGE papers, or on the order of a hundred variables in the experimental DSGE forecasting models now being constructed by central banks. What DSGE models give up in sectoral detail, they attempt to make up in logical consistency.

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