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The State of DSGE Modeling  

Paul Levine

Dynamic stochastic general equilibrium (DSGE) modeling can be structured around six key criticisms leveled at the approach. The first is fundamental and common to macroeconomics and microeconomics alike—namely, problems with rationality and expected utility maximization (EUM). The second is that DSGE models examine fluctuations about an exogenous balanced growth path and there is no role for endogenous growth. The third consists of a number of concerns associated with estimation. The fourth is another fundamental problem with any micro-founded macro-model—that of heterogeneity and aggregation. The fifth and sixth concern focus on the rudimentary nature of earlier models that lacked unemployment and a banking sector. A widely used and referenced example of DSGE modeling is the Smets-Wouters (SW) medium-sized NK model. The model features rational expectations and, in an environment of uncertainty, EUM by households and firms. Preferences are consistent with a nonstochastic exogenous balanced growth path about which the model is solved. The model can be estimated by a Bayesian systems estimation method that involves four types of representative agents (households, final goods producers, trade unions, and intermediate good producers). The latter two produce differentiated labor and goods, respectively, and, in each period of time, consist of a proportion locked into existing contracts and the rest that can reoptimize. There is underemployment but no unemployment. Finally, an arbitrage condition imposed on the return on capital and bonds rules out financial frictions. Thus the model, which has become the gold standard for DSGE macro-modeling, features all six areas of concern. The model can be used as a platform to examine how the current generation of DSGE models has developed in these six dimensions. This modeling framework has also used for macro-economic policy design.


Stock-Flow Models of Market Frictions and Search  

Eric Smith

Stock-flow matching is a simple and elegant framework of dynamic trade in differentiated goods. Flows of entering traders match and exchange with the stocks of previously unsuccessful traders on the other side of the market. A buyer or seller who enters a market for a single, indivisible good such as a job or a home does not experience impediments to trade. All traders are fully informed about the available trading options; however, each of the available options in the stock on the other side of the market may or may not be suitable. If fortunate, this entering trader immediately finds a viable option in the stock of available opportunities and trade occurs straightaway. If unfortunate, none of the available opportunities suit the entrant. This buyer or seller now joins the stocks of unfulfilled traders who must wait for a new, suitable partner to enter. Three striking empirical regularities emerge from this microstructure. First, as the stock of buyers does not match with the stock of sellers, but with the flow of new sellers, the flow of new entrants becomes an important explanatory variable for aggregate trading rates. Second, the traders’ exit rates from the market are initially high, but if they fail to match quickly the exit rates become substantially slower. Third, these exit rates depend on different variables at different phases of an agent’s stay in the market. The probability that a new buyer will trade successfully depends only on the stock of sellers in the market. In contrast, the exit rate of an old buyer depends positively on the flow of new sellers, negatively on the stock of old buyers, and is independent of the stock of sellers. These three empirical relationships not only differ from those found in the familiar search literature but also conform to empirical evidence observed from unemployment outflows. Moreover, adopting the stock-flow approach enriches our understanding of output dynamics, employment flows, and aggregate economic performance. These trading mechanics generate endogenous price dispersion and price dynamics—prices depend on whether the buyer or the seller is the recent entrant, and on how many viable traders were waiting for the entrant, which varies over time. The stock-flow structure has provided insights about housing, temporary employment, and taxicab markets.