We investigate asymmetries in the conditional mean dynamics of four sectors of the U.S. GDP data. Since the statistical evidence on nonlinearities in the conditional mean could be influenced by the presence of outliers, or by a failure to model conditional heteroskedasticity, we explicitly...

This paper uses an artificial neural network (ANN) model to forecast broad dividends, and computes fundamental stock prices with a stochastic discount factor (SDF). Broad dividends are used because they measure payouts to shareholders more accurately. Since nonlinearity is found in broad...

We study a consumption-based asset pricing model with incomplete information and [alpha]-stable shocks. Incomplete information leads to a non-Gaussian filtering problem. Bayesian updating generates fluctuating confidence in the agents' estimate of the persistent component of the dividends'...

We investigate whether fluctuations in U.S. inflation rates are better described by infrequently occurring large shocks or by frequently occurring small shocks. We estimate a model that encompasses the two hypotheses within the framework of non-Gaussian state-space models. Our results indicate...