The credit channel of monetary policy transmission: evidence from stock returns

This paper offers a novel test of the credit view of the monetary policy transmission mechanism using stock market returns. We identify Fed policy shocks using newspaper accounts and track daily stock prices immediately following the shocks. If the credit channel is important, then firms that are dependent on bank credit and internal funds should receive a relatively greater benefit (loss) from a Fed easing (tightening) than firms with access to nonbank credit at favorable terms. We identify ten policy shocks during the expansion of 1993-94 and the 'credit crunch' period of the 1990-91 recession and find little evidence supportive of an operative credit channel.     

Screening for prostate cancer using multivariate mixed-effects models

Using several variables known to be related to prostate cancer, a multivariate classification method is developed to predict the onset of clinical prostate cancer. A multivariate mixed-effects model is used to describe longitudinal changes in prostate specific antigen (PSA), a free testosterone index (FTI), and body mass index (BMI) before any clinical evidence of prostate cancer. The patterns of change in these three variables are allowed to vary depending on whether the subject develops prostate cancer or not and the severity of the prostate cancer at diagnosis. An application of Bayes' theorem provides posterior probabilities that we use to predict whether an individual will develop prostate cancer and, if so, whether it is a high-risk or a low-risk cancer. The classification rule is applied sequentially one multivariate observation at a time until the subject is classified as a cancer case or until the last observation has been used. We perform the analyses using each of the three variables individually, combined together in pairs, and all three variables together in one analysis. We compare the classification results among the various analyses and a simulation study demonstrates how the sensitivity of prediction changes with respect to the number and type of variables used in the prediction process.