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Dynamic stochastic general equilibrium
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Dynamic stochastic general equilibrium modeling (abbreviated DSGE or sometimes SDGE or DGE) is a branch of applied general equilibrium theory that is increasingly 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. One of the main reasons macroeconomists have begun to build DSGE models is that unlike more traditional macroeconometric forecasting models, DSGE macroeconomic models should not, in principle, be vulnerable to the Lucas critique (Woodford, 2003, p.
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Dynamic stochastic general equilibrium modeling (abbreviated DSGE or sometimes SDGE or DGE) is a branch of applied general equilibrium theory that is increasingly 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. One of the main reasons macroeconomists have begun to build DSGE models is that unlike more traditional macroeconometric forecasting models, DSGE macroeconomic models should not, in principle, be vulnerable to the Lucas critique (Woodford, 2003, p. 11; Tovar, 2008, p. 15).
Structure of DSGE models
As their name indicates, DSGE models are dynamic, studying how the economy evolves over time. They are also stochastic, taking into account the fact that the economy is affected by random shocks such as technological change, fluctuations in the price of oil, or errors in macroeconomic policy-making. This contrasts with the static models studied in Walrasian general equilibrium theory, applied general equilibrium models and computable general equilibrium models.
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 are technically more difficult to solve and analyze, they tend to 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, because they are founded on microeconomic principles of constrained decision-making. Therefore, DSGE models must spell out the following aspects of the economy.
- Preferences: the objectives of the agents in the economy must be specified. For example, households might be assumed to maximize a utility function over consumption and labor effort. Firms might be assumed to maximize profits.
- Technology: the productive capacity of the agents in the economy must be specified. For example, firms might be assumed to have a production function, specifying the amount of goods produced, depending on the amount of labor and capital they employ. Technological constraints on agents' decisions might also include costs of adjusting the capital stock, the level of employment, or the price level.
- Institutional framework: the institutional constraints under which economic agents interact must be specified. In many DSGE models, this might simply mean that agents make their choices within some exogenously imposed budget constraints, and that prices are assumed to adjust until markets clear. It might also mean specifying the rules of monetary and fiscal policy, or even how policy rules and budget constraints change depending on a political process.
Advantages and disadvantages of DSGE modeling
By specifying preferences (what the agents want), technology (what the agents can produce), and institutions (the way they interact), it is possible (in principle, though challenging in practice) to solve the DSGE model to predict what is actually produced, traded, and consumed. In principle, it is also possible to make valid predictions about the effects of changing the institutional framework.
In contrast, as Robert Lucas pointed out, such a prediction is unlikely to be valid in traditional macroeconometric forecasting models, since those models are based on observed past correlations between macroeconomic variables. These correlations can be expected to change when new policies are introduced, invalidating predictions based on past observations.
Given the difficulty of constructing accurate DSGE models, most central banks still rely on traditional macroeconometric models for short-term forecasting. However, the effects of alternative policies are increasingly studied using DSGE methods. Since DSGE models are constructed on the basis of assumptions about agents' preferences, it is possible to ask whether the policies considered are Pareto optimal, or how well they satisfy some other social welfare criterion derived from preferences (Woodford, 2003, p. 12).
Schools of DSGE modeling
At present two competing schools of thought form the bulk of DSGE modeling.
- Real business cycle (RBC) theory builds on the neoclassical growth model, under the assumption of flexible prices, to study how real shocks to the economy might cause business cycle fluctuations. The paper of Kydland and Prescott (1982) is often considered the starting point of RBC theory and of DSGE modeling in general. The RBC point of view is surveyed in Cooley (1995).
- New-Keynesian DSGE models build on a structure similar to RBC models, but instead assume that prices are set by monopolistically competitive firms, and cannot be instantaneously and costlessly adjusted. The paper that first introduced this framework was Rotemberg and Woodford (1997). Introductory and advanced textbook presentations are given by Galí (2008) and Woodford (2003). Monetary policy implications are surveyed by Clarida et al. (1999).
Example
The European Central Bank (ECB) has developed a DSGE model, often called the , which it uses to analyze the economy of the Eurozone as a whole (in other words, the model does not analyze individual European countries separately). The model is intended as an alternative to the Area-Wide Model (AWM), a more traditional empirical forecasting model which the ECB has been using for several years. The that describes the Smets-Wouters model also discusses the advantages of building a DSGE model instead of relying on more traditional methods.
The equations in the Smets-Wouters model describe the choices of three types of decision makers: households, who choose how much to work, to consume, and to invest; firms, which choose how much labor and capital to employ; and the central bank, which controls monetary policy. The parameters in the equations were estimated using Bayesian statistical techniques so that the model approximately describes the dynamics of GDP, consumption, investment, prices, wages, employment, and interest rates in the Eurozone economy. In order to accurately reproduce the sluggish behavior of some of these variables, the model incorporates several types of frictions that slow down adjustment to shocks, including sticky prices and wages, and adjustment costs in investment.
See also
External links
- - Website of the Society for Economic Dynamics, dedicated to advances in DSGE modeling
- - International Network for DSGE modeling, monetary and fiscal policy
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