Identification and Estimation of Dynamic Stochastic General Equilibrium Models: Skewness Matters

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This project investigates the effect of skewness on identifiability and estimability of parameters in linear and nonlinear dynamic stochastic general equilibrium (DSGE) models using new statistical distributions and econometric methods. The objective of this project is to analyze DSGE models in which skewness occurs not only exogenously in the error term distribution, but also endogenously in the decision rules of agents. This will enable one to estimate the macroeconomic implications of asymmetric production innovations, downward wage rigidities and a small but time-varying probability of disaster, and to carefully disentangle the transmission channels and effects of endogenous and exogenous skewness. Since skewness is one of the most important determinants of economic risk, the results are significant for the next generation of DSGE models in order to narrow the gap between the macroeconomic and empirical financial literature.