We use a Panel Smooth Transition Regression (STR) model to study nonlinearities in the expectationformation process in the US stock market. To this end, we use data from the Livingston survey to investigate how the importance of regressive and extrapolative expectations fluctuates over time as...

We report results on the ex ante predictability of monthly excess stock returns in Germany using real-time and revised macroeconomic data. Our real-time macroeconomic data cover the period 1994-2005. We report three results. 1) Real-time macroeconomic data did not contribute much to ex ante...

We report results on the ex ante predictability of monthly excess stock returns in Germany using real-time and revised macroeconomic data. Our real-time macroeconomic data cover the period 1994-2005. We report three results. 1) Real-time macroeconomic data did not contribute much to ex ante...

We use a machine-learning algorithm known as boosted regression trees (BRT) to implement an orthogonality test of the rationality of aggregate stock-market forecasts. The BRT algorithm endogenously selects the predictor variables used to proxy the information set of forecasters so as to maximize...

We use a boosting approach to study the time-varying out-of-sample informational content of various financial and macroeconomic variables for forecasting the volatility of gold-price fluctuations. We use an out-of-sample R2 statistic to evaluate forecasts as a function of the shape of a...