Using Leading Indicators to Forecast US Home Sales in a Bayesian VAR Framework

This paper uses Bayesian vector autoregressive models to examine the usefulness of leading indicators in predicting US home sales. The benchmark Bayesian model includes home sales, the price of homes, the mortgage rate, real personal disposable income, and the unemployment rate. We evaluate the forecasting performance of six alternative leading indicators by adding each, in turn, to the benchmark model. Out-of-sample forecast performance over three periods shows that the model that includes building permits authorized consistently produces the most accurate forecasts. Thus, the intention to build in the future provides good information with which to predict home sales. Another finding suggests that leading indicators with longer leads outperform the short-leading indicators.

Bursting Boilers and the Federal Power Redux: The Evolution of Safety on the Western Rivers

Using newly constructed data series on explosions, deaths, and steamboat traffic, we examine econometrically the causes of increased safety in steamboat boilers in the nineteenth century. Although the law of 1852 (but not that of 1838) did have a dramatic initial effect in reducing explosions, that reduction came against the background not of a system out of control but of a system that from the beginning was steadily increasing boiler safety per person- mile. The role of the federal government in conducting and disseminating basic research on boiler technology may have been more significant for increased safety than its explicit regulatory efforts.