REU 2013 – Forecasting Latent Business Conditions Using Macroeconomic Factors and the Kalman Filter


Defining and measuring a financial concept such as the current state of the economy is a difficult task. The ability to reliably forecast business conditions to hedge against adverse future market movements poses an even greater challenge, particularly in portfolio management. We consider issues involved with this type of estimation, including the use of numerous interrelated macroeconomic factors and varying information flow frequencies, and develop an underlying structure for business conditions. Our model builds on the Aruoba-Diebold-Scotti Index, proven to be effective in tracking the state of the economy. We extend this framework to accurately forecast major shifts in the economy based on intermediate data. In addition, we hope to construct our model for application in data-sparse environments, notably emerging markets.