Sentiments and Aggregate Demand Fluctuations

Abstract

We formalize the Keynesian insight that aggregate demand driven by sentiments can generate output ‡uctuations under rational expectations. When production decisions must be made under imperfect information about demand, optimal decisions based on sentiments can generate stochastic self-ful…lling rational expectations equilibria in standard economies without persistent informational frictions, externalities, non-convexities or strategic complementarities in production. The models we consider are deliberately simple, but could serve as benchmarks for more complicated equilibrium models with additional features.We construct a class of models to capture the Keynesian insight that employment and production decisions are based on expected consumer demand, and that realized aggregate demand follows …rms' production and employment decisions. Because of imperfect information in forecasting demand, consumer sentiments can matter in determining equilibrium aggregate supply. We cast the Keynesian insight in a simple dynamic stochastic general equilibrium model and characterize the rational expectations equilibria of this model. We …nd that despite the lack of any externalities orWe are indebted to George-2 equilibria of Aumann (1974, 1987) and of Maskin and Tirole (1987). 4 They emerge naturally from the endogenous signals that induce imperfectly correlated employment and output decisions by …rms. 5;6 In equilibrium there exists a distribution of sentiments such that for every realization of the sentiment shocks, the …rms' expected aggregate demand is equal to the realized aggregate demand, the consumer's expected aggregate income is equal to the realized aggregate output, and the expected prices and real wages are equal to the realized prices and real wages.The rest of the paper is organized as follows. Section 2 presents a simple benchmark model, de…nes rational expectations equilibrium, and then characterizes the fundamental equilibrium and the sentiment-driven equilibrium. In section 2.6, we provide a more abstract and streamlined model further illustrate the mechanisms behind our results. In section 2.7 we show that the fundamental equilibrium is not stable under constant gain learning, while sentiment-driven equilibrium is stable if the gain parameter is not too large. In section 3 we provide explicit microfoundations for the signal and information structures that we consider throughout the paper. Section 4 extends our analysis to other settings and Section 5 concludes.

Quantifying Confidence

“…Common-prior settings such as those studied in Angeletos and La'O (2013), Benhabib, Wang, and Wen (2015), Huo and Takayama (2015a), Nimark (2017), and Rondina and Walker (2014) can accommodate similar fluctuations in higher-order beliefs. In effect, what is "bias" in our setting becomes "rational confusion" in those settings.…”

“…Each protocol, however, is useful for different purposes. On the one hand, the protocol used in our baseline model is the same as the one assumed in the related works of Angeletos and La'O (2013), Benhabib, Wang, and Wen (2015), Huo and Takayama (2015b), and Ilut and Saijo (2016). On the other hand, the variant introduced here better captures the Keynesian spirit of demand-driven fluctuations; it seems more consistent with the idea of sluggish adjustment in aggregate demand; 25 and it boosts the degree of strategic complementarity, helping generate larger fluctuations in the macroeconomic quantities out of the same volatility in higher-order beliefs.…”

Measuring News Sentiment

“…For instance, a consumer sentiment shock has been blamed as the culprit for the 1990−91 recession (Blanchard (1993)). A number of recent papers argue that "sentiment shocks" can drive aggregate business conditions (Angeletos and La'O (2013), Benhabib, Wang, and Wen (2015), Benhabib and Spiegel (2017)). Second, sentiment may be purely informational, containing news about future states of the economy held by the public but not (yet) observed in the hard data.…”

Information-Driven Business Cycles: A Primal Approach

“…Second, going beyond the results established by Maćkowiak and Wiederholt, our result also demonstrates that one does not even need any fundamental source of business cycle fluctuations. Instead, our benchmark result establishes that purely expectational shocks in the tradition of Lorenzoni (2009), Angeletos and La'O (2013) and Benhabib, Wang and Wen (2015) can, in principle, perfectly account for business cycle data.…”

“…Among their applications, such theories offer a structural interpretation of cyclical fluctuations, formalizing the widespread idea that business cycles are driven by waves of optimism and pessimism among consumers and firms (Lorenzoni, 2009;Angeletos and La'O, 2013;Benhabib, Wang and Wen, 2015). Yet, few of these models have been investigated quantitatively, mainly because of technical difficulties arising from the introduction of dispersed information in general equilibrium frameworks and the challenge of specifying ex-ante plausible information structures.…”