Dynamic Stochastic General Equilibrium (DSGE) Model
Also known as: DSGE, dynamic stochastic general equilibrium, micro-founded macroeconomic model, Dinamik Stokastik Genel Denge Modeli (DSGE)
A DSGE model is a micro-founded macroeconomic general equilibrium model that combines the optimising decisions of households, firms, and government under rational expectations. Popularised for empirical policy work by Smets and Wouters (2007) and given its Bayesian estimation framework by An and Schorfheide (2007), it is the standard tool for central-bank policy analysis, fiscal-shock simulation, and the study of business-cycle fluctuations.
Key highlights
- Micro-founded and internally consistent: every equation derives from explicit optimisation, so policy experiments are not subject to ad hoc behavioural shifts.
- Identifies structural shocks (technology, demand, policy) from theory, allowing counterfactual policy and welfare analysis.
- The Bayesian framework lets the modeller combine prior information with the data and quantify parameter uncertainty through the posterior.
Intuition
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How it works
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When to use it
Use a DSGE model when you need a structural, internally consistent account of how a macroeconomy responds to shocks and policy, with continuous macroeconomic time series and a reasonable sample (at least about 80 observations). It fits questions in monetary and fiscal policy analysis and business-cycle research. It relies on strong assumptions: rational expectations, a chosen representative- or heterogeneous-agent structure, log-linearisation around a stationary steady state, and either a clearly stated calibration or a Bayesian estimation strategy with carefully chosen priors and checked posterior convergence.
Strengths & limitations
- Micro-founded and internally consistent: every equation derives from explicit optimisation, so policy experiments are not subject to ad hoc behavioural shifts.
- Identifies structural shocks (technology, demand, policy) from theory, allowing counterfactual policy and welfare analysis.
- The Bayesian framework lets the modeller combine prior information with the data and quantify parameter uncertainty through the posterior.
- Heavily dependent on its assumptions: rational expectations and the chosen agent structure drive the conclusions and may be unrealistic.
- Log-linearisation around a steady state can miss strongly non-linear dynamics, large deviations, and crises.
- Estimation is demanding: priors must be chosen with care, identification can be weak, and MCMC posterior convergence must be verified.
- Requires a fairly long time series (at least about 80 observations) and substantial expertise to specify and solve.
Common pitfalls
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Applications
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Frequently asked
What does 'micro-founded' mean for a DSGE model?
It means every macroeconomic relationship is derived from the explicit optimisation problems of households and firms, rather than assumed as a reduced-form correlation. This is what lets the model perform structural policy experiments without falling foul of the Lucas critique.
Should I calibrate or estimate the model?
Both are legitimate. Calibration fixes parameters to match long-run targets and theory; Bayesian estimation combines priors with the data likelihood and quantifies uncertainty via the posterior. The source stresses that the calibration-or-estimation strategy must be stated explicitly.
Why log-linearise the model?
The full equilibrium conditions are non-linear and have no closed-form solution. Log-linearising around a stationary steady state (first- or second-order perturbation) yields a tractable linear state-space system, at the cost of accuracy far from the steady state.
How is a DSGE model different from a CGE model?
DSGE models are dynamic and stochastic, built around forward-looking rational expectations and shocks over time, and are used mainly for monetary and business-cycle analysis. CGE models are typically calibrated to a Social Accounting Matrix and used to simulate the cross-sector effects of policy shocks in a Walrasian general equilibrium.
Sources
- 1.Smets, F. & Wouters, R. (2007). Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach. American Economic Review, 97(3), 586–606.
- 2.An, S. & Schorfheide, F. (2007). Bayesian Analysis of DSGE Models. Econometric Reviews, 26(2–4), 113–172.
- 3.Adjemian, S. et al. (2011). Dynare: Reference Manual, Version 4. Dynare Working Papers, 1.
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ScholarGate. (2026, June 1). DSGE Model. ScholarGate. https://scholargate.app/econometrics/dsge-model