News-Driven Business Cycles: Insights and Challenges

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 business cycles offers a coherent way of thinking about aggregate fluctuations, while at the same time we emphasize the many challenges that must be addressed before a proper assessment of the role of news in business cycles can be established. (JEL D83, D84, E13, E32, O33)

Fundamentals news, global liquidity and macroprudential policy

“…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 .…”

Bayesian inference on structural impulse response functions

“…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 ().…”

“… 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). …”

“… 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. …”

Intersectoral Labor Immobility, Sectoral Comovement, and News Shocks

“…See Beaudry and Portier () for an exhaustive list of studies that may generate the sectoral comovement in response to news shocks.…”