Working papers 

Abstract: We provide a general state space framework for estimation of the parameters of continuous-time linear DSGE models from data that are only available at discrete points in time. Our approach relies on the exact discrete-time representation of the equilibrium dynamics, which allows avoiding discretization errors. Using the Kalman filter, we construct the exact likelihood for data sampled either as stocks or flows, and estimate frequency-invariant parameters by maximum likelihood. We address the aliasing problem arising in multivariate settings and provide conditions for precluding it, which is required for local identification of the parameters in the continuous-time economic model. We recover the unobserved structural shocks at measurement times from the reduced-form residuals in the state space representation by exploiting the underlying causal links imposed by the economic theory and the information content of the discrete-time observations. We illustrate our approach using an off-the-shelf real business cycle model. We conduct extensive Monte Carlo experiments to study the finite sample properties of the estimator based on the exact discrete-time representation, and show they are superior to those based on a naive Euler-Maruyama discretization of the economic model. Finally, we estimate the model using postwar U.S. macroeconomic data, and offer examples of applications of our approach, including historical shock decomposition at different frequencies, and estimation based on mixed-frequency data.
Abstract: This paper introduces a novel approach for estimating  heterogeneous-agent macroeconomic models adding information from micro data. The methodology applies to both panels and repeated cross sections, with applications to a wide class of dynamic structural models used in macroeconomics. The routine involves the estimation of dynamic moments over subgroups of the cross-sectional dimension of agents. Micro moments differ from each other in the informative content that they carry for point estimation of the structural parameters. For instance, variability of moments over the cross-sectional distribution of households' wealth contain relevant information for the correct estimation of the subjective discount rate. However, data from the cross section are not relevant for the identification of a technology shock. 
Abstract: We document the effects  of uncertainty shocks on firm-level employment of high- and low-skilled labor. To investigate the potential effects of uncertainty on employment growth, we use that different industries are differentially exposed to a number of aggregate shocks. We use this fact to identify industry-specific uncertainty shocks. We show that while  low-skilled labor growth is negatively affected by uncertainty shocks on impact, high-skill labor growth is not. Our dynamic approach shows that high-skill labor falls with a lag. Low-skilled labor shows similar dynamics, with the effect of uncertainty being strongest one year after impact.  Our results highlight that the labor misallocation effects ascribed to uncertainty shocks seem to affect low-skilled labor most and that there is persistence in the effects. We contextualize our empirical findings within a heterogeneous firm model with high- and low-skill labor inputs and heterogenous labor adjustment costs.

Work in progress