December 5 - The DSGE Model
DSGE models trace their intellectual roots to the Real Business Cycle (RBC) tradition of the early 1980s. Pioneering work by Kydland & Prescott (1982) and Long & Plosser (1983) built micro-founded models in which macroeconomic fluctuations emerged solely from exogenous technology shocks in a frictionless setting. This was a bold response to Lucas’s critique: macroeconomic models should be grounded in stable, microeconomic decision rules, not fragile statistical correlations.
But empirical and policy limitations of RBC models—like inability to explain inflation dynamics or the impact of monetary policy—led to the integration of nominal frictions (sticky prices and wages), habit formation, adjustment costs, and imperfect competition. These elements emerged in what came to be known as New Keynesian DSGE models One of the watershed contributions in this literature is the 2003 paper by Frank Smets and Raf Wouters: “An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area” .
Key structural features:
Calvo-style sticky prices and wages with partial indexation.
Habit formation in consumption.
Capital adjustment costs and variable capacity utilization to smooth investment responses.
A suite of structural shocks: productivity, labor supply, preferences, investment frictions, government spending, cost-push shocks, and monetary policy.
Smets & Wouters deployed Bayesian estimation techniques to fit their model to Euro‑area data—GDP, consumption, investment, prices, real wages, employment, and interest rates Their model performed as well as—or better than—standard and Bayesian VAR models in capturing the dynamics of business cycles and out‑of‑sample forecasting. The model’s empirical success helped establish the approach as both credible and policy-relevant.
By embedding optimization behavior and expectations, DSGE models are robust to policy changes—addressing Lucas’s critique head-on. Their blend of rigorous micro-based structure with empirical estimation (especially Bayesian methods) gives DSGE models both theoretical legitimacy and data-fitting ability. Smets & Wouters’s model became a “workhorse” DSGE benchmark—adopted by the European Central Bank and other central banks for monetary and fiscal policy analysis As Narayana Kocherlakota—Minneapolis Fed President—has noted, their work “led DSGE models to be taken more seriously by central bankers around the world”
Dynamic Stochastic General Equilibrium (DSGE) models emerged from RBC theory, evolving to incorporate realistic frictions and policy channels. Central to this evolution was Smets & Wouters (2003), whose Bayesian-estimated, empirically powerful Euro-area model demonstrated that DSGE frameworks could both fit macroeconomic data and support policy analysis. The blend of micro foundations, empirical rigor, and policy relevance helped DSGE models become mainstream in central banking and academic macroeconomics. Though not without criticism, the DSGE enterprise continues to evolve—absorbing new data sources and structural features to remain a vital tool in macroeconomic analysis.
Sources:
Kydland, Finn E., and Edward C. Prescott. "Time to Build and Aggregate Fluctuations." Econometrica, Vol. 50, No. 6 (Nov. 1982), pp. 1345–1370.
Long, John B., Jr., and Charles I. Plosser. "Real Business Cycles." Journal of Political Economy, Vol. 91, No. 1 (Feb. 1983), pp. 39–69.
Frank Smets and Raf Wouters (2003) “An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area”.
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Prompt: “Hi, can you in 600 words describe the DSGE model in macroeconomics, where it comes from, how it emerged as such a powerful and widely used tool? Cite sources, in particular Smets and Wouters 2003.”