This paper investigates the optimal monetary policy response to a shock to collateral when policymakers act under discretion and face model uncertainty. The analysis is based on a New Keynesian model where banks supply loans to transaction constrained consumers. Our results confirm the literature on model uncertainty with respect to a cost-push shock. Insuring against model misspecification lea...

We study the robustness of active learning (AL) algorithms against prior misspecification: whether an algorithm achieves similar performance using a perturbed prior as compared to using the true prior. In both the average and worst cases of the maximum coverage setting, we prove that all α-approximate algorithms are robust (i.e., near α-approximate) if the utility is Lipschitz continuous in the...

Estimation in generalized linear mixed models for non-Gaussian longitudinal data is often based on maximum likelihood theory, which assumes that the underlying probability model is correctly specified. It is known that the results obtained from these models are not always robust against misspecification of the random-effects structure. Therefore, diagnostic tools for the detection of this missp...

Formative measurement models were first introduced in the literature more than forty years ago and the discussion about their methodological contribution has been increasing since the 1990s. However, the use of formative indicators for construct measurement in empirical studies is still scarce. This paper seeks to encourage the thoughtful application of formative models by (a) highlighting the ...

The specification of a measurement model as reflective or formative is the object of a lively debate. Part of the existing literature focuses on measurement model misspecification. This means that a true model is assumed and the impact on the path coefficients of using a wrong model is investigated. The majority of these studies is restricted to Structural Equation Modeling (SEM). Regarding PLS...