PublicationsReconciling 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 overinvestment. 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 tradeoff
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 localstability 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 limitcycle 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 PapersPutting the Cycle Back into Business Cycle Analysis (2017)with Paul Beaudry and Franck Portier (R&R, American Economic Review) Abstract: Are business cycles mainly a response to persistent exogenous shocks, or are they the consequence of strong endogenous mechanism which produces recurrent boombust phenomena? We present new evidence in favor of the second interpretation, and find support for the somewhat extreme notion that business cycles may be generated by stochastic limit cycle forces. The three elements that tend to favor this interpretation of business cycles are: (i) slightly extending the frequency window one associates with business cycle, (ii) allowing for strategic complementarities across agents arising from financial frictions, (iii) allowing for a locally unstable steady state in estimation.
Saddle Cycles: Solving Rational Expectations Models Featuring Limit Cycles (or Chaos) Using Perturbation Methods (2018)Abstract: Unlike their linear counterparts, nonlinear 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 nonlinear models is the use of perturbation methods. We show that, as typically implemented in macroeconomics, these methods are generally incapable of finding a solution to a model that features a limit cycle or chaos (even when such a solution in fact exists). As a result, when estimating a model, any parameterization that produces deterministic fluctuations would typically (and incorrectly) be discarded as being invalid. We 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 (nonlinear) manifold of the same dimension as the number of predetermined 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. We 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. 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 likelihoodbased 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 LimitCycle 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 generalequilibrium model
can give rise to limitcycle 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 microfounded mechanism in a rationalexpectations 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.
