Which pricing kernel restrictions are needed to make low dimensional Markov models consistent with given sets of predictions on aggregate stock-market fluctuations? This paper develops theoretical test conditions addressing this and related reverse engineering issues arising within a fairly...

This paper introduces a new class of parameter estimators for dynamic models, called Simulated Nonparametric Estimators (SNE). The SNE minimizes appropriate distances between nonparametric joint (or conditional) densities estimated from sample data and nonparametric joint (or conditional)...

This paper introduces a new parameter estimator of dynamic models in which the state is a multidimensional, continuous-time, partially observed Markov process. The estimator minimizes appropriate distances between nonparametric joint (and/or conditional) densities of sample data and...

This paper introduces a new class of parameter estimators for dynamic models, called simulated non-parametric estimators (SNEs). The SNE minimizes appropriate distances between non-parametric conditional (or joint) densities estimated from sample data and non-parametric conditional (or joint)...

Does capital markets uncertainty affect the business cycle? We find that financial volatility predicts 30% of post-war economic activity in the United States, and that during the Great Moderation, aggregate stock market volatility explains, alone, up to 55% of real growth. In out- of-sample...

We show that price level stabilization is not optimal in an economy where agents have incomplete knowledge about the policy implemented and try to learn it. A systematically more accommodative policy than what agents expect generates short term gains without triggering an abrupt loss of...