Dr Toru Kitagawa

We compare two approaches to using information about the signs of structural shocks at specific dates within a structural vector autoregression (SVAR): imposing ‘narrative restrictions’ (NR) on the shock signs in an otherwise set-identified SVAR; and casting the information about the shock signs as a discrete-valued ‘narrative proxy’ (NP) to point-identify the impulse responses.

How to allocate vaccines over heterogeneous individuals is one of the important policy decisions in pandemic times. This paper develops a procedure to estimate an individualized vaccine allocation policy under limited supply, exploiting social network data containing individual demographic characteristics and health status.

We propose a new specification test for assessing the validity of fuzzy regression discontinuity designs (FRD-validity).

How to allocate vaccines over heterogeneous individuals is one of the important policy decisions in pandemic times. This paper develops a procedure to estimate an individualized vaccine allocation policy under limited supply, exploiting social network data containing individual demographic characteristics and health status.

In our laboratory experiment, subjects, in sequence, have to predict the value of a good. The second subject in the sequence makes his prediction twice: first (“first belief”), after he observes his predecessor’s prediction; second (“posterior belief”), after he observes his private signal.

Many empirical questions concern target parameters selected through optimization. For example, researchers may be interested in the effectiveness of the best policy found in a randomized trial, or the best-performing investment strategy based on historical data.

This paper analyzes Structural Vector Autoregressions (SVARs) where identification of structural parameters holds locally but not globally.

Uncertainty about the choice of identifying assumptions is common in causal studies, but is often ignored in empirical practice.

In an important class of econometric problems, researchers select a target parameter by maximizing the Euclidean norm of a data-dependent vector.

This paper examines the identifying power of instrument exogeneity in the treatment effect model.

We develop methods for robust Bayesian inference in structural vector autoregressions (SVARs) where the parameters of interest are set-identified using external instruments, or ‘proxy SVARs’.

This paper reconciles the asymptotic disagreement between Bayesian and frequentist inference in set-identified models by adopting a multiple-prior (robust) Bayesian approach.

We consider two recent suggestions for how to perform an empirically motivated Monte Carlo study to help select a treatment effect estimator under unconfoundedness.

In an important class of econometric problems, researchers select a target parameter by maximizing the Euclidean norm of a data-dependent vector.

We develop methods for robust Bayesian inference in structural vector autoregressions (SVARs) where the impulse responses or forecast error variance decompositions of interest are set-identified using external instruments (or ‘proxy SVARs’).

To perform Bayesian analysis of a partially identified structural model, two distinct approaches exist: standard Bayesian inference, which assumes a single prior for the structural parameters, including the non-identified ones; and multiple-prior Bayesian inference, which assumes full ambiguity for the non-identified parameters. The prior inputs considered by these two extreme approaches can often be a poor representation of the researcher’s prior knowledge in practice.

This paper examines the asymptotic behavior of the posterior distribution of a possibly nondifferentiable function g(θ), where θ is a finite-dimensional parameter of either a parametric or semiparametric model. The main assumption is that the distribution of a suitable estimator θ^n, its bootstrap approximation, and the Bayesian posterior for θ all agree asymptotically.

We propose a new specification test for assessing the validity of fuzzy regression discontinuity designs (FRD-validity). We derive a new set of testable implications, characterized by a set of inequality restrictions on the joint distribution of observed outcomes and treatment status at the cut-off.