Reconciling Hayek's and Keynes' Views of Recessions (2018)

with Paul Beaudry and Franck Portier    The Review of Economic Studies, 85(1) (Jan. 2018)

Abstract: Recessions often happen after periods of rapid accumulation of houses, consumer durables and business capital. This observation has led some economists, most notably Friedrich Hayek, to conclude that recessions often reflect periods of needed liquidation resulting from past over-investment. According to the main proponents of this view, government spending or any other form of aggregate demand policy should not be used to mitigate such a liquidation process, as doing so would simply result in a needed adjustment being postponed. In contrast, ever since the work of Keynes, many economists have viewed recessions as periods of deficient demand that should be countered by activist fiscal policy. In this paper we reexamine the liquidation perspective of recessions in a setup where prices are flexible but where not all trades are coordinated by centralized markets. The model illustrates why liquidations likely cause recessions characterized by deficient aggregate demand and accordingly suggests that Keynes' and Hayek's views of recessions may be closely linked. In our framework, interventions aimed at stimulating aggregate demand face a trade-off whereby current stimulus postpones the adjustment process and therefore prolongs the recessions, but where some stimulative policies may nevertheless remain desirable.

Is the Macroeconomy Locally Unstable and Why Should We Care?

with Paul Beaudry and Franck Portier    NBER Macroeconomics Annual 31 (2016)

Abstract: In most modern macroeconomic models, the steady state (or balanced growth path) of the system is a local attractor, in the sense that, in the absence of shocks, the economy would converge to the steady state. In this paper, we examine whether the time series behavior of macroeconomic aggregates (especially labor market aggregates) is in fact supportive of this local-stability view of macroeconomic dynamics, or if it instead favors an alternative interpretation in which the macroeconomy may be better characterized as being locally unstable, with nonlinear deterministic forces capable of producing endogenous cyclical behavior. To do this, we extend a standard AR representation of the data to allow for smooth nonlinearities. Our main finding is that, even using a procedure that may have low power to detect local instability, the data provide intriguing support for the view that the macroeconomy may be locally unstable and involve limit-cycle forces. An interesting finding is that the degree of nonlinearity we detect in the data is small, but nevertheless enough to alter the description of macroeconomic behavior. We complete the paper with a discussion of the extent to which these two different views about the inherent dynamics of the macroeconomy may matter for policy.

Working Papers

Putting the Cycle Back into Business Cycle Analysis (2018)

with Paul Beaudry and Franck Portier    (R&R, American Economic Review)

Abstract: Are business cycles mainly a response to persistent exogenous shocks, or do they instead reflect a strong endogenous mechanism which produces recurrent boom-bust phenomena? In this paper we present new evidence in favor of the second interpretation and, most importantly, we highlight the set of key elements that influence our answer to this question. In particular, when adopting our most preferred estimation framework, we find support for the somewhat extreme notion that business cycles may be generated by stochastic limit cycle forces; that is, we find support for the notion that business cycles may primarily reflect an endogenous propagation mechanism buffeted only by temporary shocks. The three elements that tend to favor this type of interpretation of business cycles are: (i) slightly extending the frequency window one associates with business cycle phenomena, (ii) allowing for strategic complementarities across agents that arise due to financial frictions, and (iii) allowing for a locally unstable steady state in estimation. We document the sensitivity of our findings to each of these elements within the context of an extended New Keynesian model with real-financial linkages.

Saddle Cycles: Solving Rational Expectations Models Featuring Limit Cycles
(or Chaos) Using Perturbation Methods (2018)

Unlike their linear counterparts, non-linear models of the business cycle are in some cases capable of generating sustained economic fluctuations even in the absence of shocks (e.g., via limit cycles or chaos). A popular approach to solving non-linear models is the use of perturbation methods. I show that, as typically implemented in macroeconomics, these methods are generally incapable of finding solutions that feature limit cycles or chaos, a fact that does not appear to be well understood in the literature. As a result of this shortcoming, when estimating a model, any parameterization that produces deterministic fluctuations would typically (and incorrectly) be discarded as being invalid. I propose a modification to the standard perturbation algorithm that addresses this shortcoming. Fundamentally, the method is one most macroeconomists are familiar with: look for the (non-linear) manifold of the same dimension as the number of pre-determined variables on which trajectories remain bounded. In standard perturbation algorithms, it is assumed that trajectories on this manifold in fact converge to the steady state, which fundamentally excludes the possibility of limit cycles or chaos. I relax this assumption, extending the standard algorithm to also consider manifolds on which trajectories do not converge to a steady state as long as they do not become unbounded.

Older Projects

Misspecification and the Causes of Business Cycles (2015)

Abstract: Modern dynamic stochastic general equilibrium (DSGE) macroeconomics models generally feature several different exogenous shock processes. A standard tool in the quantitative macroeconomics toolbox for evaluating the individual importance of these shocks is a variance decomposition. The reliability of this tool depends importantly on having accurate estimates of the variances of the innovations to the exogenous shock processes. Using a novel framework, I show that when the DSGE model is misspecified and the shock variances are estimated using a likelihood-based approach, the resulting estimates are biased upward. Next, using the same framework, I propose a simple procedure to identify and partially correct for the effects of model misspecification on these variance estimates. As an example of its usefulness, I apply this procedure to a recent paper and find that it reduces the estimated variances of the shocks in the model by as much as a third of their respective naive estimates.

Can a Limit-Cycle Model Explain Business Cycle Fluctuations? (2014)

Abstract: In conventional models of the business cycle, all fluctuations are ultimately caused by the arrival of random shocks. As a result, individual booms and busts are largely unrelated phenomena. An alternative to this viewpoint is that booms and busts are inherently related, which suggests that fluctuations are at least in part driven by deterministic cyclical forces.This paper shows (1) how a purely deterministic general-equilibrium model can give rise to limit-cycle fluctuations, and (2) that this model can replicate business cycle features once it includes a small amount of exogenous variation. The deterministic limit cycle arises through a simple micro-founded mechanism in a rational-expectations environment, and does not rely on the existence of multiple equilibria or dynamic indeterminacy. Since these cycles would indefinitely repeat themselves in the absence of shocks, a TFP shock is introduced in order to create irregularities. The model is estimated to match US hours data, and is shown to be able to match the data closely. The TFP shock in the model is of a reasonable persistence and relatively small size, accounting for only around a fifth of the standard deviation of hours in the model. This highlights that models capable of generating deterministic fluctuations do not require the addition of large, persistent shocks in order to match patterns in the data, which is a common criticism of conventional models.