Recoverability and Expectations-Driven Fluctuations

Abstract

Time series methods for identifying structural economic disturbances often require disturbances to satisfy technical conditions that can be inconsistent with economic theory. We propose replacing these conditions with a less restrictive condition called recoverability, which only requires that the disturbances can be inferred from the observable variables. As an application, we show how shifting attention to recoverability makes it possible to construct new identifying restrictions for technological and expectational disturbances. In a vector autoregressive example using postwar U.S. data, these restrictions imply that independent disturbances to expectations about future technology are a major driver of business cycles.

Sentimental Business Cycles

“…An alternative mechanism arises in models with imperfect information where noise shocks about fundamentals can generate economic fluctuations. As demonstrated by Chahrour and Jurado (2018), models with news shocks about fundamentals also have noise representations. Thus, one can think of our formulation as implementing the Chahrour and Jurado (2018) procedure to separate shocks into t and n t .…”

“…Forecasters is included in a structural estimation of a DSGE model, Faccini and Melosi (2019) find that sentiment shocks are important drivers of boom-bust cycles. Similarly, Lorenzoni (2009), Beaudry, Nam andWang (2011), Forni, Gambetti, Lippi andSala (2017), Chahrour andJurado (2018), andEnders, Kleemann andMuller (2020) conclude in favour of considerable impact of noise and sentiment shocks on aggregate outcomes using a variety of approaches. Our analysis differs from these papers in the use of an external instrument for sentiment shocks and, therefore, in our attempt to provide direct evidence on the causal effects of this type of shock without hard wiring the identification to a specific DSGE model.…”

“…Our article comes closer in nature to papers that have adopted external instruments for the identification of other shocks such as Mertens and Ravn (2013) who estimate the impact of tax shocks using a tax narrative as an external instrument, Gertler and Karadi (2015) who estimate monetary policy shocks using high-frequency identified monetary news as an external instrument, or Miranda-Agrippino, Hoke and Bluwstein (2019) who identify technology news shocks using patent applications as an instrument. In an interesting recent contribution, Chahrour and Jurado (2022) also use a VAR approach to estimate a TFP noise shock which moves expectations about future TFP but not TFP itself. They find this source of expectational shocks to account for a significant fraction of GDP fluctuations in the US at business cycle frequencies.…”

Large Bayesian VARs with Factor Stochastic Volatility: Identification, Order Invariance and Structural Analysis

“…However, in the formulation above, Φ 0,t is identified due to Σ t being restricted to a diagonal matrix and L being identified by sign restrictions as described in Section 3. Hence, impulse response functions and forecast error variance decompositions to all shocks in u t are identified, even though u t is generally not recoverable from past and future observations of y t , as defined in Chahrour and Jurado (2021).…”

Instrumental Variable Identification of Dynamic Variance Decompositions

“…Our proposed test of invertibility is related to the Granger causality tests developed in SVAR settings by Giannone & Reichlin (2006) and Forni & Gambetti (2014). Finally, the weaker notion of "recoverability" studied here has independently been proposed by Chahrour & Jurado (2020) outside the context of external IV identification. 1…”