Luca Neri

Publications

Invalid proxies and volatility changes

with G. Angelini and L. Fanelli

Journal of Business & Economic Statistics

[web, OA, software, arXiv:2403.08753]

Estimation of continuous-time linear DSGE models from discrete-time measurements

with B.J. Christensen and J.C. Parra-Alvarez

Journal of Econometrics (2024)

[web, OA, software]

Working papers

Beyond validity: SVAR identification through the proxy zoo (with J. Huang)

[ArXiv:2601.11195, highlight]

Abstract: This paper develops a framework for robust identification in SVARs when researchers face a zoo of proxy variables. Instead of imposing exact exogeneity, we introduce generalized ranking restrictions (GRR) that bound the relative correlation of each proxy with the target and non-target shocks through a continuous proxy-quality parameter. Combining GRR with standard sign and narrative restrictions, we characterize identified sets for structural impulse responses and show how to partially identify the proxy-quality parameter using the joint information contained in the proxy zoo. We further develop sensitivity and diagnostic tools that allow researchers to assess transparently how empirical conclusions depend on proxy exogeneity assumptions and the composition of the proxy zoo. A simulation study shows that proxies constructed from sign restrictions can induce biased proxy-SVAR estimates, while our approach delivers informative and robust identified sets. An application to U.S. monetary policy illustrates the empirical relevance and computational tractability of the framework.

Uncovering attention heterogeneity (with J. Boccanfuso) Revise and resubmit at the Journal of Monetary Economics

[latest version, Discussion Paper]

Abstract: Heterogeneity in individuals’ attention to news shapes how aggregate shocks propagate through the economy. We propose a novel method to uncover the full distribution of attention in surveys of consumers and professional forecasters, by developing a finite mixture model to cluster forecasts according to their level of attention. Our results reveal substantial heterogeneity in attention, which, when ignored, leads to an overestimation of average attention. Leveraging our clustering, we construct decade-long panel datasets on attention and provide new insights into its drivers. We document an asymmetric effect of aggregate shocks on the distribution of attention and assess how attention varies with socioeconomic characteristics. Finally, we identify systematic deviations from Bayesian updating and related theories designed to account for behavioral biases. In particular, we find that individuals’ attention is less sensitive to changes in uncertainty and signal precision than predicted by these theories. Overall, this paper opens new avenues by providing the first panel datasets on attention and illustrating how they can inform theories of imperfect information through within-individual variations.

Measuring attention when forecasts are rounded (with J. Boccanfuso)

[latest version]

Abstract: Rounding in survey data obscures the identification of key parameters. We show that this issue is both conceptually and empirically important for estimating two measures of attention: the fraction of individuals who adjust their forecasts and the weight they place on news when doing so. We propose a simple fix-treating forecast revisions as interval data-which restores point identification. Our results reveal that many zero revisions arise from rounding rather than non-adjustment, and that even when individuals adjust, they remain largely inattentive.

Structural estimation combining micro and macro data

[latest version, SSRN id:3911041]

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.

Firm uncertainty and labor composition dynamics (with E. H. Partsch)

[latest version]

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

Estimation of weakly identified parameters with macroeconomic and financial data (with B. J. Christensen and M. van der Wel)

Dissertation

Essays on Estimation of Dynamic Macroeconomic Models (link)

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