On an international post World War II dataset, we use an iterated GMM procedure to estimate and test the Campbell and Cochrane (1999) habit formation model with a time-varying risk-free rate. In addition, we analyze the predictive power of the surplus consumption ratio for future stock and bond...

We suggest an iterated GMM approach to estimate and test the consumption based habit persistence model of Campbell and Cochrane (1999), and we apply the approach on annual and quarterly Danish stock and bond returns. For comparative purposes we also estimate and test the standard CRRA model. In...

When the consumption growth rate is measured based upon fourth quarter data, it tracks predictable variation in future excess stock returns. Low fourth quarter consumption growth rates predict high future excess stock returns such that expected returns are high at business cycle troughs and low...

We study the role of sentiment variables as predictors for US recessions. We combine sentiment variables with either classical recession predictors or common factors based on a large panel of macroeconomic and financial variables. Sentiment variables hold vast predictive power for US recessions...

This paper uses an iterated GMM approach to estimate and test the consumption based habit persistence model of Campbell and Cochrane [Campbell, J.Y., Cochrane, J.H., 1999. By force of habit: A consumption-based explanation of aggregate stock market behavior. Journal of Political Economy 107,...

This paper finds empirical support for the habit persistence model of Campbell and Cochrane (1999) along both cross-sectional and time-series dimensions of the US stock market over the period 1947-2005. GMM estimations show that the model is able to explain a substantial part of the...

We show that macroeconomic growth at the end of the year (fourth quarter or December) strongly influences expected returns on risky financial assets, whereas economic growth during the rest of the year does not. We find this pattern for many different asset classes, across different time...

We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves...