Research

Macroprudential Intervention and (Un)employed Households

This paper studies indirect macroprudential intervention’s effects on household welfare in a two-agent New Keynesian setting. I develop a two-agent New Keynesian DSGE model à la Gertler and Karadi (2011) to compare the welfare impacts of different monetary policy regimes in the presence of a tax policy in the banking system. I investigate whether there is a welfare benefit if a standard Taylor rule incorporates financial variables, in particular, the interest rate spread. Our results suggest that deviating from the standard Taylor rule to its augmented alternative in an unregulated economy is ineffective regarding welfare improvement. On the other hand, within a regulated economy, the maximized welfare of households is given in the presence of a tax policy and a monetary policy rule reacting to the interest rate spread. However, the results are unclear about the welfare-improving role of monetary policy in terms of economic stabilization within both unregulated and regulated economies.

AI-assisted teams outperform AI-led teams but not human-only teams in assessing research reproducibility in quantitative social science, with A. Brodeur et al., PNAS, 2026

Large Language Models (LLMs) such as ChatGPT are transforming how scientists conduct and validate research, offering promise as tools to improve scientific reproducibility. However, computational reproducibility and error detection remain expensive and labor-intensive. We experimentally test how collaboration between researchers and LLM assistants influences the reproduction of quantitative social science findings across different levels of AI autonomy. We randomly assigned 288 researchers to 103 teams working under three conditions: human-only, AI-assisted (using ChatGPT as a collaborative tool), or AI-led (ChatGPT operating with minimal human oversight). Teams reproduced published results from leading social science journals, detected coding errors, and proposed robustness checks. Human-only and AI-assisted teams achieved comparable reproduction rates (94% vs. 91%) and performed similarly on most outcomes, except human-only teams identified significantly more major coding errors. Both substantially outperformed AI-led teams, which achieved only a 37% reproduction rate, detected fewer errors across all categories, proposed weaker robustness checks, and required more time. This autonomous approach, however, likely represents only a lower bound of AI capabilities. Despite rapid model advances, expert human judgment currently remains indispensable for reliable empirical verification. While AI assistance did not degrade most outcomes, it provided no measurable advantages and was associated with reduced detection of major errors. However, the 37% autonomous reproduction rate indicates that AI could provide value in settings where scale or cost constraints preclude human review of papers, even though general-purpose LLMs offer no immediate advantages for human-supervised verification.

Reproducibility and Robustness of Economics and Political Science Research, with A. Brodeur et al., Nature, 2026, [WP Version]

This systematic and large-scale reproduction effort tests the reproducibility and robustness of economics and political science, contributing to a growing literature on research credibility and self-correction in science. We reproduced original analyses and conducted robustness checks for 110 articles recently published in leading economics and political science journals, all of which have mandatory data and code sharing policies. We found that over 85% of published claims were computationally reproducible. In robustness checks, our re-analyses led to 72% of statistically significant estimates to remain significant and in the same direction, and the median reproduced effect size is (nearly) the same as the originally published effect size (that is, 99% of the published effect size). Additionally, six independent research teams examined 12 pre-specified hypotheses about determinants of robustness. Research teams with more experience found lower levels of robustness, and robustness correlated with neither author characteristics nor data availability. 

Relative Risk Aversion: A Meta-Analysis, with T. Havranek,  and Z. Irsova, Journal of Economic Surveys, 2025

Estimates of relative risk aversion vary widely, but no study has attempted to quantitatively trace the sources of the variation. We collect 1,021 estimates from 92 studies that use the consumption Euler equation to measure relative risk aversion and that disentangle it from intertemporal substitution. We show that calibrations of risk aversion are systematically larger than estimates thereof. Moreover, reported estimates are systematically larger than the underlying risk aversion because of publication bias. After correction for the bias, the literature suggests a mean risk aversion of 1 in economics and 2–7 in finance contexts. The reported estimates are driven by the characteristics of data (frequency, dimension, country, stockholding) and utility (functional form, treatment of durables). To obtain these results, we use recently developed nonlinear techniques to correct for publication bias and Bayesian model averaging techniques to account for model uncertainty. 

The Calvo Parameter Revisited: An Unbiased Insight, Applied Economics Letters, 2025

This study provides a meta-analysis of the Calvo parameter estimated within the new Keynesian Phillips curve using a data set of 509 estimates from 40 studies published in a quarter century. Novel linear and non-linear techniques suggest publication bias distorting the reported estimates towards typical values of the Calvo parameter used for calibration. Moreover, Bayesian model averaging results indicate that the reported estimates are systematically affected by various aspects of research design, particularly the choice of forcing variable in the NKPC, instrument selection, and authors’ affiliations.

Intertemporal Substitution in Labor Supply: A Meta-Analysis, with T. Havranek, R. Horvath, and Z. Irsova, Review of Economic Dynamics, 2023

The intertemporal substitution (Frisch) elasticity of labor supply governs the predictions of real business cycle models, New Keynesian models, and models of taxation. We show that the mean reported estimates, and consequently calibrations, are exaggerated due to publication bias. For both the intensive and extensive margins, the literature provides over 700 estimates, with a mean of 0.5 in both cases. Correcting for publication bias and emphasizing quasi-experimental evidence reduces the mean intensive margin elasticity to 0.2 and renders the extensive margin elasticity tiny. An aggregate hours elasticity of about 0.25 is the most consistent with empirical evidence. To trace the differences in reported elasticities to differences in estimation context, we collect 23 variables reflecting study design and employ Bayesian and frequentist model averaging to address model uncertainty. On both margins, the elasticity is systematically larger for women and workers near retirement, but not enough to support an aggregate hours elasticity above 0.5. 

Contagious Defaults in Interbank Networks, Czech Journal of Economics and Finance, 2022 

This paper investigates systemic risk and contagion processes in an interbank network using network science methods. The interbank network is studied to understand the contagion process within a network considering differences in the network structure and the characteristics of components. Simulations support the claim that heterogeneous networks are more resilient to contagious shocks, while these shocks are more problematic in homogeneous networks. This paper also shows that more interconnections among banks could accelerate or block the contagion process, depending on the structure of the network and the seniority of debts in the interbank network. 

Attention Allocation and Monetary Policy with N. Buliskeria (draft coming soon)

Anatomy of New Keynesian Phillips Curve with N. Buliskeria, and Z. Irsova (draft coming soon)

Do Two Wrongs Make a Right? Publication and Attenuation Biases in Economics (draft coming soon)