This paper proposes decomposing macroeconomic fluctuations into three components based on their contributions to fluctuations of different frequencies. A first component explains business cycle fluctuations but has no long-run effect; a second component also influences business cycles but may have a long-run effect; a third component has only long-run effects. The decomposition has several desirable properties. The two business cycle components jointly explain 99% of business cycle fluctuations and deliver comovements consistent with a structural interpretation as demand and supply shocks, respectively. The presence of long-run, non-business cycle fluctuations biases full-information estimation of business cycle models, however, distorting conclusions about the sources of business cycles. Estimating a model to match only the business cycles shocks resolves this issue and gives more plausible parameter estimates.

This paper investigates whether the documented shortcomings of medium-scale DSGE models, as outlined by Beaudry et al. (2020), stem from parameter misspecification or a fundamental absence of endogenous propagation mechanisms. Beaudry et al. (2020) identify that the spectral density of “hours worked” displays two distinct peaks, one at business cycle frequencies and another at lower frequencies. They argue that medium-scale DSGE models fail to accurately capture both the position and magnitude of the spectral density peak at business cycle frequencies, attributing this limitation to insufficient endogenous mechanisms and the restrictive nature of AR(1) shocks. In contrast, we argue, both analytically and through model simulations, that these models can reproduce the spectral density peak at business cycle frequencies when AR(1) shocks are employed, provided there is additional endogenous propagation in labor markets. Importantly, this propagation mechanism has significant implications for counterfactual policy analysis.

In an interconnected society, social networks grow through formation of strategic connections based on the hierarchy within the social network. Often, the hierarchy becomes self-reinforcing and the observed valuations of the individuals in the hierarchy become disconnected from the corresponding fundamentals. We propose a network model to characterize the disconnect between the observed and fundamental valuations of entities, where the difference is a function of the linkages across the entities. In a growing social network, new entrants come at every point of time and offer connections to the incumbents based on the observed valuations. Individuals care only about their ranks in the hierarchy of observed valuation. With myopic individuals, network grows in equilibrium, but the associated hierarchy becomes unstable. However, with farsighted individuals, the network growth process is hierarchy-preserving and depending on the structure of seed network, the process may be completely halted by individuals who have incentives to preserve hierarchy. These two mechanisms taken together provide a comprehensive characterization of valuation in a growing inter-connected, hierarchical society. We illustrate an application of the model by analyzing the Indian board interlocking network. Our model enables us to find the hierarchy of the board members’ network and to identify the dispersion in magnitude of network externalities across directors.