Conference Agenda

A damage function measures quantitatively how aggregated economies respond to climate change and it has been used as a powerful tool to provide trajectories of future economic development. However, the specification of the damage function remains highly contentious. In this paper we extend the conventional damage function by introducing interactive terms between temperature and precipitation. Our new specification allows for heterogeneous responses to climate change in different climate conditions, making possible the response to temperature change dependent on precipitation levels, and vice versa. The results show that all temperature, precipitation, as well as their interaction are statistically significant factors affecting economic growth.The most sensitive economy to climate change is the combination of cold temperature with excessive precipitation, in which case, either reduced rainfall or a warming trend could benefit economic growth considerably such as in Canada and Northern Europe countries. Compared to cold climate economies, economies with moderate to warm climates are more resilient to precipitation change, which could possibly be attributed to their adaptation to climates characterizing high variability in precipitation.

This paper aims at capturing time-varying spillover effects with panel data. We consider panel models where the outcome of a unit not only depends on its characteristics (private effects) and also on the characteristics of other units (spillover effects). The private effects can be unit-specific or homogeneous. We allow the linkage structure, i.e., which units affect which, to be latent. Moreover, the structure and the spillover effects may both change at an unknown breakpoint. To estimate the breakpoint, linkage structure, spillover effects, and private effects, we solve a penalized least squares optimization and employ double machine learning procedures to improve the convergence rate and statistical inferences. We establish the super consistency of the breakpoint estimator, which allows us to make inferences on other parameters as if the breakpoint was known. We illustrate the theory via simulated and empirical data.

We propose a new unified approach to identifying and estimating spatio-temporal dependence structures in large panels. The model accommodates global cross-sectional dependence due to global dynamic factors as well as local cross-sectional dependence, which may arise from local network structures. Model selection, filtering of the dynamic factors, and estimation are carried out iteratively using a new algorithm that combines the Expectation-Maximization algorithm with proximal minimization, allowing us to efficiently maximize an l1- and l2-penalized state space likelihood function. A Monte Carlo simulation study illustrates the good performance of the algorithm in terms of determining the presence and magnitude of common factors and local spillover effects. In an empirical application, we investigate monthly US interest rate data on 12 maturities over almost 35 years. We find that there are heterogeneous local spillover effects among neighboring maturities. Taking this local dependence into account improves out-of-sample forecasting performance.

During its first two years, the SARS-CoV-2 pandemic manifested as multiple waves shaped by complex interactions between variants of concern, non-pharmaceutical interventions, and the immunological landscape of the population. Understanding how the age-specific epidemiology of SARS-CoV-2 evolved throughout the pandemic is crucial for informing policy. In this paper, we develop an inference-based modeling approach to reconstruct the
burden of true infections and hospital admissions in children, adolescents,
and adults over the seven waves of four variants (wild-type,
Alpha, Delta, Omicron BA.1) during the first two years of the pandemic,
using the Netherlands as the motivating example. We find that
reported cases are a considerable underestimate and a generally poor
predictor of true infection burden, especially because case reporting
differs by age. The contribution of children and adolescents to
total infection and hospitalization burden increased with successive
variants and was largest during the Omicron BA.1 period. However,
the ratio of hospitalizations to infections decreased with each subsequent
variant in all age categories. During the Delta and Omicron BA.1 periods, primary infections were
common in children but relatively rare in adults who experienced
either re-infections or breakthrough infections. Our approach can be
used to understand age-specific epidemiology through successive
waves in other countries where random community surveys are absent but basic surveillance and
statistics data are available

We introduce a quasi-Bayesian technique designed to relax the rigidity of moment conditions,
thereby accommodating model misspecification. We establish new Bernstein–von mises (BvM)
type theorems for the quasi-posterior distributions. We also demonstrate the practical usefulness
of this method through simulation exercises and empirical application to linear instrumental
variable (IV) models and nonlinear quantile regression models. Our results show that this approach still provides informative inference even when there is a significant degree of misspecification.