I estimate and evaluate a model with a representative agent who is concerned that the persistence properties of her baseline model of consumption and inflation are misspecified. Coping with model uncertainty, she discovers a pessimistically biased worst-case model that dictates her behavior. I...

We introduce a robust approach to the study of optimal dynamic pricing in the face of information arrival. As opposed to classical intertemporal pricing models, we consider consumers who learn about their willingness-to-pay for a product over time (which is typical for initially unfamiliar...

Kleijnen proposed using Ordinary Least Squares method combining with experimental design to estimate polynomial regression metamodels, but I/O data violates some classical assumptions of OLS as the correlation between output which due to common random numbers and Heterogeneous variances which...

This paper studies a robustness of solutions in finite depth of reasoning models. The level-k and cognitive hierarchy models conventionally assume that each player has a commonly known anchor behavior to obtain a sharp prediction. We provide a general framework to examine whether the obtained...

We introduce the Gerber statistic, a robust measure of correlation. The statistic extends Kendall's Tau by counting the proportion of simultaneous co-movements in series when their amplitudes exceed data-dependent thresholds. This is unlike the standard Pearson correlation that is sensitive to...

Online Appendix- Further Robustness Tests- An online appendix to "Over-the-Counter Market Liquidity and Securities Lending".Full Publication: "http://ssrn.com/abstract=3340251" Over-the-Counter Market Liquidity and Securities Lending

In many applications common in testing for convergence the number of cross-sectional units is large and the number of time periods are few. In these situations asymptotic tests based on an omnibus null hypothesis are characterised by a number of problems. In this paper we propose a multiple...

This paper studies estimation of linear panel regression models with heterogeneous coefficients, when both the regressors and the residual contain a possibly common, latent, factor structure. Our theory is (nearly) efficient, because based on the GLS principle, and also robust to the...

We study the quantitative potential of DSGE models with incomplete information. In contrast to existing literature, we offer predictions that are robust across all possible private information structures that agents may have. Our approach maps DSGE models with information-frictions into a...