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In the window mean-payoff objective, given an infinite path, instead of considering a long run average, we consider the minimum payoff that can be ensured at every position of the path over a finite window that slides over the entire path. Chatterjee et al. studied the problem to decide if in a two-player game, Player 1 has a strategy to ensure a window mean-payoff of at least 0. In this work, we consider a function that given a path returns the supremum value of the window mean-payoff that can be ensured over the path and we show how to compute its expected value in Markov chains and Markov decision processes. We consider two variants of the function: Fixed window mean-payoff in which a fixed window length $l_{max}$ is provided; and Bounded window mean-payoff in which we compute the maximum possible value of the window mean-payoff over all possible window lengths. Further, for both variants, we consider (i) a direct version of the problem where for each path, the payoff that can be ensured from its very beginning and (ii) a non-direct version that is the prefix independent counterpart of the direct version of the problem., Comment: Replaced PP-hardness of direct fixed window objective with PSPACE-hardness, added alternative definition of window mean-payoff

We explore quantitatively the possibility of multiple equilibria in a model of sovereign debt crises. The source of multiplicity is the one identified by Calvo (1988). This type of multiplicity has been at the heart of the policy debate through the recent European sovereign debt crisis. Key for multiplicity in the model is a stochastic process for output featuring long periods of either high or low growth. We calibrate the output process in the model using data for the southern European countries that were exposed to the debt crisis. We find that expectations-driven sovereign debt crises are empirically plausible, but only in periods of stagnation. Multiplicity is state dependent: in periods of stagnation and for intermediate levels of debt, interest rates may be high for reasons unrelated to fundamentals. [ABSTRACT FROM AUTHOR]

We develop a multi-sector DSGE model to calculate upstream and downstream industry exposure networks from U.S. input-output tables and test the relative importance of shocks from each direction by comparing these with estimated networks of firms' equity return responses to one another. The correlations between the upstream exposure and equity return networks are large and statistically significant, while the downstream exposure networks have lower -- but still positive -- correlations that are not statistically significant. These results suggest a low short-term elasticity of substitution across inputs transmitting shocks from suppliers, but more flexible ties with downstream firms. Additionally, both the DSGE model and simulations of our empirical approach highlight the importance of accounting for common factors in network estimation, which become more important over our 1989-2017 sample period, explaining 11.7% of equity return variation over the first ten years and 35.0% over the final ten. [ABSTRACT FROM AUTHOR]

Applied researchers interested in estimating key parameters of DSGE models face an array of choices regarding numerical solution and estimation methods. We focus on the likelihood evaluation of models with occasionally binding constraints. We document how solution approximation errors and likelihood misspecification, related to the treatment of measurement errors, can interact and compound each other. [ABSTRACT FROM AUTHOR]