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Working Paper
Forecasting with Sufficient Dimension Reductions
Factor models have been successfully employed in summarizing large datasets with few underlying latent factors and in building time series forecasting models for economic variables. When the objective is to forecast a target variable y with a large set of predictors x, the construction of the summary of the xs should be driven by how informative on y it is. Most existing methods first reduce the predictors and then forecast y in independent phases of the modeling process. In this paper we present an alternative and potentially more attractive alternative: summarizing x as it relates to y, so ...
Working Paper
A Unified Framework for Dimension Reduction in Forecasting
Factor models are widely used in summarizing large datasets with few underlying latent factors and in building time series forecasting models for economic variables. In these models, the reduction of the predictors and the modeling and forecasting of the response y are carried out in two separate and independent phases. We introduce a potentially more attractive alternative, Sufficient Dimension Reduction (SDR), that summarizes x as it relates to y, so that all the information in the conditional distribution of y|x is preserved. We study the relationship between SDR and popular estimation ...
Report
Corporate Bond Market Distress
We link bond market functioning to future economic activity through a new measure, the Corporate Bond Market Distress Index (CMDI). The CMDI coalesces metrics from primary and secondary markets in real time, offering a unified measure to capture access to debt capital markets. The index correctly identifies periods of distress and predicts future realizations of commonly used measures of market functioning, while the converse is not the case. We show that disruptions in access to corporate bond markets have an economically material, statistically significant impact on the real economy, even ...