Consumer misperceptions, uncertain fundamentals, and the business cycle

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract This paper explores the importance of shocks to consumer misperceptions, or "noise shocks", in a quantitative business cycle model. I embed imperfect information as in Lorenzoni (2009) into a new Keynesian model with price and wage rigidities. Agents learn about the components of labor productivity by only observing aggregate productivity and a noisy signal. Noise shocks lead to expectational errors about the true fundamentals triggering aggregate fluctuations. Estimating the model with Bayesian methods on US data shows that noise shocks contribute to 20 percent of consumption fluctuations at short horizons. Wage rigidity is pivotal for the importance of noise shocks. Terms of use: Documents in EconStor may

Animal Spirits, Fundamental Factors and Business Cycle Fluctuations

“…For instance, Blanchard et al (2013) show that such results are robust when they embed the same productivity process and information structure in a small-scale DSGE model, including investment and capital accumulation, nominal rigidities and a monetary policy rule. In our empirical exercise, we will use the structural model of Hürtgen (2014), whose predictions will be the basis of our VAR model restrictions.…”

“…Moreover, the larger model allows us to study the effects of noise shocks on other variables, like output, stock prices, interest rates and inflation. The sign restrictions of our larger model are based on the predictions resulting from the structural model of Hürtgen (2014).…”

“…Moreover, with a two-variable VAR, we assume that only two structural shocks drive the data. Therefore, in this section we enrich the VAR model by including additional variables and add additional identifying restrictions that rely on a larger structural model as described in Hürtgen (2014). The extended setup allows us to identify possible additional sources of shocks and see the impact of noise shocks on additional variables, such as stock prices or consumer sentiment.…”

“…This approach is first used to show how the superior information set of the econometrician can be separated into more precise information and information that is available at a later date. We apply thereafter this method to U.S. data and identify supply and noise shocks in two separate VAR models: a simple two-variable VAR model relying on restrictions based on our theoretical model and a large-scale VAR model where the empirical restrictions on predictions are derived from the full structural model of Hürtgen (2014). Our empirical exercises show that the identified supply and noise shocks have effects as predicted by our theoretical model.…”

“…Blanchard et al (2013) explore the role of noisy information in cyclical developments empirically, separating fluctuations that are due to genuine changes in supply (news shocks) from those due to temporary expectational errors (noise shocks). They show that the identification of those shocks is only possible via the estimation of a full structural model, such as the one in Barsky and Sims (2012) or Hürtgen (2014). Since economic agents face a signal extraction problem when separating news from noise shocks, the econometrician, using the same data, cannot use structural VARs to recover such shocks.…”