Model Complexity and Prediction Error in Macroeconomic Forecasting

This research project extends proven techniques in statistical learning theory so that they cover the kind of models and data of most interest to macroeconomic forecasting.

The dominant modeling traditions among academic economists, namely dynamic stochastic general equilibrium (DSGE) and vector autoregression (VAR) models, both spectacularly failed to forecast the financial collapse and recession which began in 2007 or even to make sense of its course after the fact. Largely unnoticed by economists, over the last three decades statisticians and computer scientists have developed sophisticated methods of model selection and forecast evaluation, under the rubric of statistical learning theory. These methods have revolutionized pattern recognition and artificial intelligence, and the modern industry of data mining would not exist without it. Economists’ neglect of this theory is especially unfortunate, since it could be of great help in resolving macroeconomic disputes and determining the reliability of whatever models emerge for macroeconomic time series. This project extends these techniques and exploits the fact that major alternatives can all be put in the form of state-space models.