Can urban coffee consumption help predict US inflation?

Motivated by the importance of coffee to Americans and the significance of the coffee subsector to the US economy, we pursue three notable innovations. First, we augment the traditional Phillips curve model with the coffee price as a predictor, and show that the resulting model outperforms the traditional variant in both in-sample and out-of-sample predictability of US inflation. Second, we demonstrate the need to account for the inherent statistical features of predictors such as persistence, endogeneity, and conditional heteroskedasticity effects when dealing with US inflation. Consequently, we offer robust illustrations to show that the choice of estimator matters for improved US inflation forecasts. Third, the proposed augmented Phillips curve also outperforms time series models such as autoregressive integrated moving average and the fractionally integrated version for both in-sample and out-of-sample forecasts. Our results show that augmenting the traditional Phillips curve with the urban coffee price will produce better forecast results for US inflation only when the statistical effects are captured in the estimation process. Our results are robust to alternative measures of inflation, different data frequencies, higher order moments, multiple data samples and multiple forecast horizons

Economic policy uncertainty and industry return predictability – Evidence from the UK

This paper examines whether local, regional, and global policy uncertainty shocks predict the sector returns of the UK stock market. Consistent with the market integration literature, we find global policy uncertainty shock is the major predictor of sector returns. Our second contribution is that the predictability of returns is dependent on the state of the business cycle. Finally, the evidence of predictability is strongest at the 6-month horizon, revealing that the impact of policy uncertainty shocks lasts for a few months. Our findings hold even after controlling for well-known risk factors and different sub-samples of data