Business Cycle Anatomy

Abstract

We propose a new strategy for dissecting the macroeconomic time series, provide a template for the business-cycle propagation mechanism that best describes the data, and use its properties to appraise models of both the parsimonious and the medium-scale variety. Our findings support the existence of a main business-cycle driver but rule out the following candidates for this role: technology or other shocks that map to TFP movements; news about future productivity; and inflationary demand shocks of the textbook type. Models aimed at accommodating demanddriven cycles without a strict reliance on nominal rigidity appear promising."One is led by the facts to conclude that, with respect to the qualitative behavior of comovements among series, business cycles are all alike. To theoretically inclined economists, this conclusion should be attractive and challenging, for it suggests the possibility of a unified explanation of business cycles." Lucas (1977) 1 IntroductionIn their quest to explain macroeconomic fluctuations, macroeconomists have often relied on models in which a single, recurrent shock acts as the main business-cycle driver. 1 This practice is grounded not only on the desire to offer a parsimonious, unifying explanation as suggested by Lucas, but also on the property that such a model may capture diverse business-cycle triggers if these share a common propagation mechanism: multiple shocks that produce similar responses for all variables of interest can be considered as essentially the same shock. 2 Is there evidence of such a common propagation mechanism in macroeconomic data? And if yes, how does it look like?We address these questions with the help of a new empirical strategy. The strategy involves taking multiple cuts of the data. Each cut corresponds to a SVAR-based shock that accounts for the maximal volatility of a particular variable over a particular frequency band. Whether these empirical objects have a true structural counterpart in the theory or not, their properties form a rich set of cross-variable, static and dynamic restrictions, which can inform macroeconomic theory. We call this set the "anatomy."A core subset of the anatomy is the collection of the five shocks obtained by targeting the main macroeconomic quantities, namely unemployment, output, hours worked, consumption and investment, over the business-cycle frequencies. These shocks turn out to be interchangeable in the sense of giving rise to nearly the same impulse response functions (IRFs) for all the variables, as well as being highly correlated with one another.The interchangeability of these empirical shocks supports parsimonious theories featuring a main, unifying, propagation mechanism. Their shared IRFs provide an empirical template of it.In combination with other elements of our anatomy, this template rules out the following candidates for the main driver of the business cycle: technology or other shocks that map to TFP movements; news about future productivity; and inflationary demand shocks of the textbook type.Prominent ...

Quantifying Confidence

“…Second, the value of ρ assumed here is close to the one estimated in the next section in the context of two medium‐scale, DSGE models. Finally, to the extent that the fluctuations induced by in our model resemble either the “demand shock” identified in Blanchard and Quah () or the “main business‐cycle shock” identified in Angeletos, Collard, and Dellas (), our parameterization is consistent with the evidence in those papers as well.…”

“…Huo and Takayama () obtained quantitative findings that are broadly consistent with ours while maintaining the common‐prior assumption. Angeletos, Collard, and Dellas () provided VAR‐based evidence that the business cycle in the U.S. data can be explained by a shock that has similar properties to the one we have accommodated in our theory. Levchenko and Pandalai‐Nayar () provided additional corroborating evidence in an international context.…”

“…Additional support is provided by the evidence in a companion paper ( Angeletos, Collard, and Dellas ()), where we use a SVAR approach to document that the bulk of the business‐cycle volatility in output, hours, investment, and consumption in U.S. data can be accounted for by a single shock whose IRFs look very much like those seen in Figure . A similar picture is also painted in Section , where the confidence shock emerges as the main driver of the business cycle within medium‐scale DSGE models that contain multiple other shocks.…”

“…The evidence provided in a companion paper ( Angeletos, Collard, and Dellas ()) also speaks against a Phillips‐curve‐centric explanation of the business cycle and in favor of a mechanism like the one accommodated here: in that paper, we use a SVAR approach to document that the bulk of the business‐cycle variation in employment, output, investment, and consumption can be explained by a single shock that can be thought of as a “non‐inflationary demand shock” in the sense that it triggers strong co‐movement between the aforementioned quantities at the business‐cycle frequencies without commensurate co‐movements in either TFP and labor productivity or inflation at any frequency. What is more, the empirical IRFs of the shock identified in that paper are actually quite similar to the theoretical IRFs of the confidence shock in the present paper.…”

Finding a Stable Phillips Curve Relationship: A Persistence-Dependent Regression Model

“…A nascent body of research, however -building upon earlier work by Comin and Gertler (2006) -is exploring the frequency domain for clues about business cycle drivers and dynamics. Angeletos, Collard, and Dellas (2020) have recently argued that assessing the root causes of business cycles requires delving into the drivers of unemployment, output, consumption, and the like at different frequencies. Beaudry, Galizia, and Portier (2020) likewise highlight mediumfrequency cycles and argue that it is crucial to examine the properties of the data at different frequencies in order to discriminate across classes of models.…”