How to Solve Dynamic Stochastic Models Computing Expectations Just Once

By Kenneth Judd, Lilia Maliar and Serguei Maliar

http://d.repec.org/n?u=RePEc:nbr:nberwo:17418&r=dge

We introduce a technique called “precomputation of integrals” that makes it possible to compute conditional expectations in dynamic stochastic models in the initial stage of the solution procedure. This technique can be applied to any set of equations that contains conditional expectations, in particular, to the Bellman and Euler equations. After the integrals are precomputed, we can solve stochastic models as if they were deterministic. We illustrate the benefits of precomputation of integrals using one- and multi-agent numerical examples.

My selection this week is more about technique than answering a question. Yet, this may be an interesting development in simplifying the computations of equilibria of models with large state spaces (many agents), especially for those with idiosyncratic and aggregate shocks.

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Uncertainty Shocks in a Model of Effective Demand

By Susanto Basu and Brent Bundick

http://d.repec.org/n?u=RePEc:boc:bocoec:774&r=dge

This paper examines the role of uncertainty shocks in a one-sector, representative-agent dynamic stochastic general-equilibrium model. When prices are flexible, uncertainty shocks are not capable of producing business-cycle comovements among key macro variables. With countercyclical markups through sticky prices, however, uncertainty shocks can generate fluctuations that are consistent with business cycles. Monetary policy usually plays a key role in offsetting the negative impact of uncertainty shocks. If the central bank is constrained by the zero lower bound, then monetary policy can no longer perform its usual stabilizing function and higher uncertainty has even more negative effects on the economy. Calibrating the size of uncertainty shocks using fluctuations in the VIX, we find that increased uncertainty about the future may indeed have played a significant role in worsening the Great Recession, which is consistent with statements by policymakers, economists, and the financial press.

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Another paper that studies the current uncertain environment, with a disheartening result that the Fed can currently do nothing about it. It would be interesting to see in which way this result also applies to an environment where there is policy uncertainty.

Labor-Market Heterogeneity, Aggregation, and the Policy-(In)variance of DSGE Model Parameters

By Yongsung Chang, Sun-Bin Kim and Frank Schorfheide

http://d.repec.org/n?u=RePEc:roc:rocher:566&r=dge

Data from a heterogeneous-agents economy with incomplete asset markets and indivisible labor supply are simulated under various fiscal policy regimes and an approximating representative-agent model is estimated. Preference and technology parameter estimates of the representative-agent model are not invariant to policy changes and the bias in the representative-agent model’s policy predictions is large compared to predictive intervals that reflect parameter uncertainty. Since it is not always feasible to account for heterogeneity explicitly, it is important to recognize the possibility that the parameters of a highly aggregated model may not be invariant with respect to policy changes.

Interesting warning that a representative agent DSGE model may not be “deep” enough to take fully into account the Lucas Critique. Indeed, distribution matters sometimes and may bias our estimates of the deepest parameters.