We propose two novel methods to "bring ABMs to the data". First, we put forward a new Bayesian procedure to estimate the numerical values of ABM parameters that takes into account the time structure of simulated and observed time series. Second, we propose a method to forecast aggregate time...

This paper proposes a Bayesian estimation framework for panel-data sets with binary dependent variables where a large number of cross-sectional units is observed over a short period of time, and cross-sectional units are interdependent in more than a single network domain. The latter provides...

We examine the incentives and implications of supplier encroachment, when final good produc-tion requires the use of multiple complementary inputs and the entry of a supplier into the final good market gives rise to mutual outsourcing of inputs between the encroaching supplier and the incumbent....

We present a general framework for Bayesian estimation and causality assessment in epidemiological models. The key to our approach is the use of sequential Monte Carlo methods to evaluate the likelihood of a generic epidemiological model. Once we have the likelihood, we specify priors and rely...

Employing an endogenous growth model with human capital, this paper explores how productivity shocks in the goods and human capital producing sectors contribute to explaining aggregate fluctuations in output, consumption, investment and hours. Given the importance of accounting for both the...

We examine how financial crises redistribute risk, employing novel empirical methods and micro data from the largest financial crisis of the 20th century – the Great Depression. Using balance-sheet and systemic risk measures at the bank level, we build an econometric model with incidental...

Gender differences in self-confidence could explain women’s under representation in high-income occupations and glass-ceiling effects. We draw lessons from the economic literature via a survey of experts and a Bayesian hierarchical model that aggregates experimental findings over the last...

During the last decades, the United States has applied increasingly high trade protection against China. We combine detailed information on US antidumping (AD) duties — the most widely used trade barrier — with US input-output data to study the effects of trade protection along supply...

Recent disruptions to global value chains (GVCs) have raised an important question: Can decoupling from GVCs increase a country’s welfare by reducing its exposure to foreign supply shocks? We use a quantitative trade model to simulate GVCs decoupling, defined as increased barriers to global...