Abstract:There is a widespread belief that changes in expectations may be an important independent driver of economic fluctuations. The news view of business cycles offers a formalization of this perspective. In this paper we discuss mechanisms by which changes in agents' information, due to the arrival of news, can cause business cycle fluctuations driven by expectational change, and we review the empirical evidence aimed at evaluating their relevance. In particular, we highlight how the literature on news and busines… Show more
“…The stochastic process of y T t follows a standard Markov process to be specified later, and is influenced by the arrival of noisy news, along the lines of the literature on news and business cycles (see Beaudry and Portier (2014) for a recent survey). In particular, every period the representative agent receives noisy news that relates to the future evolution of y T t .…”
We study optimal macroprudential policy in a model in which unconventional shocks, in the form of news about future fundamentals and regime changes in world interest rates, interact with collateral constraints in driving the dynamics of financial crises. These shocks strengthen incentives to borrow in good times (i.e. when "good news" about future fundamentals coincide with a low-world-interest-rate regime), thereby increasing vulnerability to crises and enlarging the pecuniary externality due to the collateral constraints. Quantitatively, an optimal schedule of macroprudential debt taxes can lower the frequency and magnitude of financial crises, but the policy is complex because it features significant variation across interest-rate regimes and news realizations.
“…The stochastic process of y T t follows a standard Markov process to be specified later, and is influenced by the arrival of noisy news, along the lines of the literature on news and business cycles (see Beaudry and Portier (2014) for a recent survey). In particular, every period the representative agent receives noisy news that relates to the future evolution of y T t .…”
We study optimal macroprudential policy in a model in which unconventional shocks, in the form of news about future fundamentals and regime changes in world interest rates, interact with collateral constraints in driving the dynamics of financial crises. These shocks strengthen incentives to borrow in good times (i.e. when "good news" about future fundamentals coincide with a low-world-interest-rate regime), thereby increasing vulnerability to crises and enlarging the pecuniary externality due to the collateral constraints. Quantitatively, an optimal schedule of macroprudential debt taxes can lower the frequency and magnitude of financial crises, but the policy is complex because it features significant variation across interest-rate regimes and news realizations.
“…As explained in Section , structural macro models with news shocks often exhibit noninvertible IRFs, giving the SVMA method a distinct advantage over SVARs, as the latter assume away noninvertibility. Beaudry and Portier () surveyed the evolving news shock literature. Recent empirically minded contributions include Benati, Chan, Eisenstat, and Koop ( ), Sims (), Arezki, Ramey, and Sheng (), and Chahrour and Jurado ().…”
Section: Application: News Shocks and Business Cyclesmentioning
confidence: 99%
“… See Alessi, Barigozzi, and Capasso (, Section 4–6), Blanchard, L'Huillier, and Lorenzoni (, Section II), Leeper, Walker, and Yang (, Section 2), and Beaudry and Portier (, Section 3.2). …”
mentioning
confidence: 99%
“… Sims and Zha (), Fève and Jidoud (), Sims (), Beaudry and Portier (, Section 3.2), and Beaudry, Fève, Guay, and Portier ( ) argued that noninvertibility need not cause large biases in SVAR estimation if forward‐looking variables are available. Forni et al () and Forni, Gambetti, and Sala () used information from large panel data sets to ameliorate the omitted variables problem; based on the same idea, Giannone and Reichlin () and Forni and Gambetti () proposed tests of invertibility. …”
I propose to estimate structural impulse responses from macroeconomic time series by doing Bayesian inference on the Structural Vector Moving Average representation of the data. This approach has two advantages over Structural Vector Autoregressions. First, it imposes prior information directly on the impulse responses in a flexible and transparent manner. Second, it can handle noninvertible impulse response functions, which are often encountered in applications. Rapid simulation of the posterior distribution of the impulse responses is possible using an algorithm that exploits the Whittle likelihood. The impulse responses are partially identified, and I derive the frequentist asymptotics of the Bayesian procedure to show which features of the prior information are updated by the data. The procedure is used to estimate the effects of technological news shocks on the U.S. business cycle.
Sectoral comovement of output and hours worked is a prominent feature of business cycle data. However, most two-sector neoclassical models fail to generate this sectoral comovement. We construct and estimate a two-sector neoclassical Dynamic Stochastic General Equilibrium (DGSE) model generating sectoral comovement in response to both anticipated and unanticipated shocks. The key to our model's success is a significant degree of intersectoral labor immobility, which we estimate using data on sectoral hours worked. Furthermore, we demonstrate that imperfect intersectoral labor mobility provides a better explanation for the sectoral comovement than an alternative model emphasizing the role of labor-supply wealth effects.JEL codes: E13, E32
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.