Hamilton Fout

IMPERFECT TRANSMISSION OF TECHNOLOGY SHOCKS AND THE BUSINESS CYCLE CONSEQUENCES

We investigate the business cycle effects of imperfect transmission of technology shocks within a basic real business cycle (RBC) model along two dimensions. First, we assume that agents cannot distinguish a temporary increase in productivity growth from a sustained increase in the underlying growth rate of productivity and instead must conduct signal extraction exercises and update beliefs about the source of aggregated shocks. Second, we propose a technology adjustment cost resulting in the slow diffusion of technological innovations into the production process. Both of these impediments to the transmission of technology result in a large initial wealth effect, increasing investment and hours less, relative to the usual RBC model without these frictions. Furthermore, each of these features is capable of producing a decline in hours on impact of the technology shock matching the negative response in hours found in the data by such works as Gali [American Economic Review 89(1), 249–271 (1999)].

Macroeconomic Learning and the Propagation of Technology Shocks

We incorporate learning in a standard dynamic stochastic general equilibrium model along two empirically-supported dimensions. First, we assume that agents cannot directly observe the individual components of the productivity shock and instead must conduct signal extraction exercises and update beliefs about the source of aggregate shocks. For this type of learning to have qualitative impacts on dynamic behavior we must assume either a counterfactually high relative variance or a large persistence parameter on the trend component of productivity. Second, we propose an alternative learning mechanism in which technological innovations diffuse slowly through the economy. This mechanism is successful at generating a variety of empirically observed responses under reasonable parameterizations.

Home Equity Conversion Mortgages: The Secondary Market Investor Experience

We analyze Fannie Mae’s experience with the Home Equity Conversion Mortgage product. From 1993-2010, Fannie Mae acquired 492,465 of these loans, representing 75% of the total market. During this period, prior to recent program changes, credit screening was not an element in the underwriting process. Using loan and borrower characteristics, we model the probability of adverse terminations; and given adverse termination, loss severity. We then show how the addition of credit information affects our models. Finally, we use credit data to provide a counterfactual assessment of the extent to which better screening might have affected portfolio performance. We find that imposing a minimum borrower credit score of 620 would have prevented 22.7% of loans with credit information from being originated and, simultaneously, would have reduced adverse terminations by 31% and subsequent losses by nearly

ASSIGNING CONFIDENCE VALUES TO AUTOMATED PROPERTY VALUATIONS BY USING THE NON-TYPICAL PROPERTY CHARACTERISTICS OF THE PROPERTIES

250 million, or 32%.