News, noise, and fluctuations: an empirical exploration

TL;DR: In this article, the authors examine whether this view is consistent with the data and reach three main conclusions: structural estimation methods typically cannot recover news and noise shocks, if agents face a signal extraction problem, and are unable to separate news from noise, then the econometrician, faced with either the same data as the agents or a subset of these data, cannot do it either.

Abstract: A common view of the business cycle gives a central role to anticipations. Consumers and firms continuously receive information about the future, which is sometimes news and sometimes just noise. Based on this information, consumers and firms choose spending and, because of nominal rigidities, spending affects output in the short run. If ex post the information turns out to be news, the economy adjusts gradually to a new level of activity. If it turns out to be just noise, the economy returns to its initial state. Therefore, the dynamics of news and noise generate both short-run and long-run changes in aggregate activity. This view appears to capture many of the aspects often ascribed to fluctuations: the role of animal spirits in affecting demand—spirits coming here from a rational reaction to information about the future—the role of demand in affecting output in the short run, together with the notion that in the long run output follows a natural path determined by fundamentals. In this paper, we examine whether this view is consistent with the data. We reach three main conclusions, the first two methodological, the third substantive. Structural VARs typically cannot recover news and noise shocks. The reason is straightforward: if agents face a signal extraction problem, and are unable to separate news from noise, then the econometrician, faced with either the same data as the agents or a subset of these data, cannot do it either. While structural estimation methods cannot recover the actual time series for news and noise shocks either, they can recover underlying structural parameters, and thus the relative role and dynamic effects of news and noise shocks. Estimation of both a simple model, and then of a more elaborate DSGE model suggest that agents indeed solve such a signal extraction problem, and that noise shocks play an important role in determining short-run dynamics. Recent efforts to estimate business cycle models in which expectations about the future play an important role include Christiano et al. (2010) and Schmitt-Grohe and Uribe (2012). Those papers follow the approach of Jaimovich and Rebelo (2009) and model news as perfectly anticipated productivity changes that will occur at

Figures

Table 4: Variance Decomposition: Robustness Checks

Figure 4: Smoothed estimates of the permanent component of productivity, of long-run productivity, and of consumers’ real time expectations Top panel: smoothed estimate of xt (solid line) and of xt|t (dashed line) Bottom panel: smoothed estimate of xt+∞ (solid line) and of xt+∞|t (dashed line)

Figure 7: Normalized RMSE of the estimated shocks at time t using data up to t + j

Table 2: Maximum Likelihood Estimation: Benchmark Model

Table 3: Maximum Likelihood Estimation: Unconstrained Model

Figure 6: Smoothed estimates of the shocks

Table 6: Maximum Likelihood Estimation: Slow Consumption Adjustment

Table 7: Maximum Likelihood Estimation: Labor hoarding

Figure 5: RMSE of the estimated states at time t using data up to t + j

Figure 3: Variance Decomposition: Benchmark Model

Figure 1: Impulse Responses to the Three Shocks

Table 5: Maximum Likelihood Estimation: standard New Keynesian model

Table 1: Consumption and Productivity Regressions. Note: Sample: 1970:1 to 2008:1. ∆(j)a ≡ a(+j − 1) − a(−1). In parenthesis: robust standard errors computed using the Newey-West window and 10 lags.

Figure 2: True and SVAR-based estimated impulse responses