Asymmetric effects of monetary policy in regional housing markets

The responsiveness of house prices to monetary policy shocks depends on the nature of the shock – expansionary versus contractionary – and on local housing supply elasticities. These findings are established based on a panel of 263 US metropolitan areas. We test and find supporting evidence for the hypothesis that expansionary monetary policy shocks have a larger impact on house prices when supply elasticities are low. Our results also suggest that contractionary shocks are orthogonal to supply elasticities, as implied by downward rigidity of housing supply. A standard theoretical conjecture is that contractionary shocks have a greater impact on house prices than expansionary shocks, as long as supply is not perfectly inelastic. For areas with high housing supply elasticity, our results are in line with this conjecture. However, for areas with an inelastic housing supply, we find that expansionary shocks have a greater impact on house prices than contractionary shocks. We provide evidence that the direction of the asymmetry is related to a momentum effect that is more pronounced when house prices are increasing than when they are falling

Focused Bayesian Prediction

We propose a new method for conducting Bayesian prediction that delivers accurate predictions without correctly specifying the unknown true data generating process. A prior is defined over a class of plausible predictive models. After observing data, we update the prior to a posterior over these models, via a criterion that captures a user-specified measure of predictive accuracy. Under regularity, this update yields posterior concentration onto the element of the predictive class that maximizes the expectation of the accuracy measure. In a series of simulation experiments and empirical examples we find notable gains in predictive accuracy relative to conventional likelihood-based prediction

Residential Investment and Recession Predictability

We assess the importance of residential investment in predicting economic recessions for an unbalanced panel of 12 OECD countries over the period 1960Q1-2014Q4. Our approach is to estimate various probit models with different leading indicators and evaluate their relative prediction accuracy using the receiver operating characteristic curve. We document that residential investment contains information useful in predicting recessions both in-sample and out-of-sample. This result is robust to adding typical leading indicators, such as the term spread, stock prices, consumer condence surveys and oil prices. It is shown that residential investment is particularly useful in predicting recessions for countries with high homeownership rates. Finally, in a separate exercise for the US economy, we show that the predictive ability of residential investment is robust to employing real-time data.publishedVersio

Quantifying time-varying forecast uncertainty and risk for the real price of oil

We propose a novel and numerically efficient quantification approach to forecast uncertainty of the real price of oil using a combination of probabilistic individual model forecasts. Our combination method extends earlier approaches that have been applied to oil price forecasting, by allowing for sequentially updating of time-varying combination weights, estimation of time-varying forecast biases and facets of miscalibration of individual forecast densities and time-varying inter-dependencies among models. To illustrate the usefulness of the method, we present an extensive set of empirical results about time-varying forecast uncertainty and risk for the real price of oil over the period 1974-2018. We show that the combination approach systematically outperforms commonly used benchmark models and combination approaches, both in terms of point and density forecasts. The dynamic patterns of the estimated individual model weights are highly time-varying, reflecting a large time variation in the relative performance of the various individual models. The combination approach has built-in diagnostic information measures about forecast inaccuracy and/or model set incompleteness, which provide clear signals of model incompleteness during three crisis periods. To highlight that our approach also can be useful for policy analysis, we present a basic analysis of profit-loss and hedging against price risk.publishedVersio