The Role of Credit in Predicting US Recessions
We study the role of credit in forecasting US recession periods with probit models. We employ both classical recession predictors and common factors based on a large panel of financial and macroeconomic variables as control variables. Our findings suggest that a number of credit variables are useful predictors of US recessions over and above the control variables both in and out of sample. Especially the excess bond premium, capturing the cyclical changes in the relationship between default risk and credit spreads, is found to be a powerful predictor. Overall, models that combine credit variables, common factors, and classic recession predictors, are found to have the best forecasting performance.
This paper analyzes the use of CBERS (China-Brazil Earth Resources Satellite) high resolution images to map urban limits of a small city in the Amazon Region of Brazil. The use of Sino-Brazilian satellite images to study small cities of the Brazilian Amazon Region is also discussed. A HCR image was used to classify the image of the study area into four classes: urban, forest, clouds, and shadows, by visual interpretation. This map was compared with another map produced with unsupervised classification. A confusion matrix was produced to evaluate the accuracy of unsupervised classification. Comparison between the reference map (visual interpretation) and the unsupervised classification map verified that 82.8% of the areas were classified correctly. This ability to recognize and delimitate small cities in Amazonia demonstrates the quality of the CBERS – HCR image.