The Discipline of Operations Management and Econometrics
Forecast combination in discrete choice models: predicting FOMC monetary policy decisions
Dr Laurent Pauwels, The Discipline of Operations Management and Econometrics, The University of Sydney
30th Jul 2010 11:00 am - Room 498, Merewether Building
This paper extends the discrete choice model by Hu and Phillips (2004) that allows for nonstationary dependent and explanatory variables. It provides a new methodology to combine in- and out-of-sample forecasts based on a mixture of discrete models. This is achieved primarily by combining probabilities associated with each model. The methodology is not limited to point forecast and can be used to predict the whole density of the multiple choice model. Scoring functions, such as log-score and quadratic score, are used to evaluate the forecasting performance of the diverse models. We apply this methodology to forecast the outcomes of Federal Reserve board meetings decisions in changing the federal funds target rate. The original and extended Hu and Phillips (2004) data set and model are employed as a starting point to conduct the empirical studies. This paper also investigate the utilisation of real-time data, which contains the actual information available at the time when Federal Reserve board makes decisions rather than revised and updated data series. Furthermore, models are constructed with a mixture of data frequencies.
This paper is work-in-progress and only preliminary results and preliminary ideas will be presented. This is a joint work of Laurent Pauwels and Andrey Vasnev.