“Matching” is a statistical technique used to evaluate the effect of a treatment by comparing the treated and non-treated units in an observational study. Matching provides an alternative to older estimation methods, such as ordinary least squares (OLS), which involves strong assumptions...

Despite growing interest in the use of complex models, such as machine learning (ML) models, for credit underwriting, ML models are difficult to interpret, and it is possible for them to learn relationships that yield de facto discrimination. How can we understand the behavior and potential...

This paper develops identification and estimation methods for dynamic structural models when agents' actions are unobserved by econometricians. We provide conditions under which choice probabilities and latent state transition rules are non-parametrically identified with a continuous state...

We describe how to use the composite likelihood to ameliorate estimation, computational, and inferential problems in dynamic stochastic general equilibrium models. We present a number of situations where the methodology has the potential to resolve well-known problems. In each case we consider,...

We focus on two particular aspects of model risk: the inability of a chosen model to fit observed market prices at a given point in time (calibration error) and the model risk due to recalibration of model parameters (in contradiction to the model assumptions). In this context, we follow the...

The paper studies how parameter variation affects the decision rules of a DSGE model and structural inference. We provide diagnostics to detect parameter variations and to ascertain whether they are exogenous or endogenous. Identification and inferential distortions when a constant parameter...

The analysis of highly structured data requires models with unobserved components (random effects) able to adequately account for the patterns of variances and correlations. The specification of the unobserved components is a key and challenging task. In this paper, we first review the...