Approximating stochastic processes by finite-state Markov chains is useful for reducing computational complexity when solving dynamic economic models. We provide a new method for accurately discretizing general Markov processes by matching low order moments of the conditional distributions using...

The standard generalized method of moments (GMM) estimation of Euler equations in heterogeneous-agent consumption-based asset pricing models is inconsistent under fat tails because the GMM criterion is asymptotically random. To illustrate this, we generate asset returns and consumption data from...

I study the asset pricing implications and the efficiency of a tractable dynamic stochastic general equilibrium model with heterogeneous agents and incomplete markets along the lines of Krebs (2003a). Contrary to previous applications of these types of models, I find that generically the...

We provide evidence suggesting that the cross-sectional distributions of U.S. consumption and its growth rate obey the power law in both the upper and lower tails, with exponents approximately equal to 4. Consequently, high order moments are unlikely to exist, and the GMM estimation of Euler...