We propose the double robust Lagrange multiplier (DRLM) statistic for testing hypotheses specified on the minimizer of the population continuous updating objective function. The (bounding) χ2 limiting distribution of the DRLM statistic is robust to both misspecification and weak identification,...

In many set-identified models, it is difficult to obtain a tractable characterization of the identified set. Therefore, researchers often rely on nonsharp identification conditions, and empirical results are often based on an outer set of the identified set. This practice is often viewed as...

Evaluating policy in imperfectly competitive markets requires understanding firm behavior. While researchers test conduct via model selection and assessment, we present the advantages of Rivers and Vuong (2002) (RV) model selection under misspecification. However, degeneracy of RV invalidates...

We propose the double robust Lagrange multiplier (DRLM) statistic for testing hypotheses specified on the minimizer of the population continuous updating objective function. The (bounding) χ2 limiting distribution of the DRLM statistic is robust to both misspecification and weak identification,...

Structural estimation in economics often makes use of models formulated in terms of moment conditions. While these moment conditions are generally well-motivated, it is often unknown whether the moment restrictions hold exactly. We consider a framework where researchers model their belief about...

Structural estimation in economics often makes use of models formulated in terms of moment conditions. While these moment conditions are generally well-motivated, it is often unknown whether the moment restrictions hold exactly. We consider a framework where researchers model their belief about...

This research seeks to highlight a common mistake that researchers in the area of Operations and Supply Chain Management (O&SCM) make when selecting the measurement models in Structural Equation Modelling. In fat, the unproper selection of a measurement model in Structural Equation Modeling...

Mental models help people navigate complex environments. This paper studies how people deal with model uncertainty. In an experiment, participants estimate a company's value, facing uncertainty about which one of two models correctly determines its true value. Using a between-subjects design, we...

Agent-Based Models (ABMs) provide powerful tools for economic analysis, capturing microto-macro interactions and emergent properties. However, integration with empirical data has been a persistent challenge. To address it, we propose a protocol for integration between empirical data and ABM,...

This paper investigates how different parametrisations of the monetary policy reaction function and different mechanisms of expectation formation shape the macroeconomic outcomes in the estimated Smets-Wouters type of DSGE model. The initial macroeconomic conditions of the simulations correspond...

We offer a contribution to the analysis of optimal monetary policy. The standard approach to determine what policy rule a central bank should follow is to take a single structural model and minimize the unconditional volatilities of inflation and real activity. In this paper, we propose monetary...

Policy makers express their views and decisions via the lens of a particular model or theory. But since any model is a highly stylized representation of the unknowable object of interest, all these models are inherently misspecified, and the resulting ambiguity injects uncertainty in the...

Evaluating policy in imperfectly competitive markets requires understanding firm behavior. While researchers test conduct via model selection and assessment, we present the advantages of Rivers and Vuong (2002) (RV) model selection under misspecification. However, degeneracy of RV invalidates...

In many set-identified models, it is difficult to obtain a tractable characterization of the identified set. Therefore, researchers often rely on nonsharp identification conditions, and empirical results are often based on an outer set of the identified set. This practice is often viewed as...

This paper proposes an information-based inference method for partially identified parameters in incomplete models that is valid both when the model is correctly specified and when it is misspecified. Key features of the method are: (i) it is based on minimizing a suitably defined...

We study for a benchmark small open emerging economy an optimal robust monetary policy à la Hansen and Sargent (2003) considering additive model uncertainty. The robust control approach supposes that economic agents are not able to assign probabilities to a set of all plausible models and...

Deriving an optimal asset allocation hinges crucially on the quality of inputs used in the optimization. If the vector of expected returns and the covariance matrix are known with certainty, mean-variance optimization produces optimal portfolios. If, however, these parameters are estimated with...

This paper proposes an information-based inference method for partially identified parameters in incomplete models that is valid both when the model is correctly specified and when it is misspecified. Key features of the method are: (i) it is based on minimizing a suitably defined...