We analyze the importance of demand from emerging and developed economies as drivers of the real price of oil over the last two decades. Using a factor-augmented vector autoregressive (FAVAR) model that allows us to distinguish between different groups of countries, we find that demand from emerging economies (most notably from Asian countries) is more than twice as important as demand from developed countries in accounting for the fluctuations in the real price of oil and in oil production. Furthermore, we find that different geographical regions respond differently to oil supply shocks and oilspecific demand shocks that drive up oil prices, with Europe and North America being more negatively affected than emerging economies in Asia and South America. We demonstrate that this heterogeneity in responses is not only attributable to differences in energy intensity in production across regions but also to degree of openness and the investment share in GDP.

In this paper, we use U.S. real-time data to produce combined density nowcasts of quarterly GDP growth, using a system of three commonly used model classes. We update the density nowcast for every new data release throughout the quarter, and highlight the importance of new information for nowcasting. Our results show that the logarithmic score of the predictive densities for U.S. GDP growth increase almost monotonically, as new information arrives during the quarter. While the ranking of the model classes changes during the quarter, the combined density nowcasts always perform well relative to the model classes in terms of both logarithmic scores and calibration tests. The density combination approach is superior to a simple model selection strategy and also performs better in terms of point forecast evaluation than standard point forecast combinations.JEL-codes: C32, C52, C53, E37, E52.

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Traditional studies of the Dutch disease do not account for productivity spillovers between the booming resource sector and other domestic sectors. We put forward a simple theory model that allows for such spillovers. We then identify and quantify these spillovers using a Bayesian Dynamic Factor Model (BDFM). The model allows for resource movements and spending effects through a large panel of variables at the sectoral level, while also identifying disturbances to the commodity price, global demand and non-resource activity. Using Australia and Norway as representative cases studies, we find that a booming resource sector has substantial productivity spillovers on non-resource sectors, effects that have not been captured in previous analysis. That withstanding, there is also evidence of two-speed economies, with non-traded industries growing at a faster pace than traded. Furthermore, commodity prices also stimulate the economy, but primarily if an increase is caused by higher global demand. Commodity price growth unrelated to global activity is less favourable, and for Australia, there is evidence of a Dutch disease effect with crowding out of the tradable sectors. As such, our results show the importance of distinguishing between windfall gains due to volume and price changes when analysing the Dutch disease hypothesis.JEL-codes: C32, E32, F41, Q33

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No abstract

We analyze the importance of demand from emerging and developed economies as drivers of the real price of oil over the last two decades. Using a factor-augmented vector autoregressive (FAVAR) model that allows us to distinguish between different groups of countries, we find that demand from emerging economies (most notably from Asian countries) is more than twice as important as demand from developed countries in accounting for the fluctuations in the real price of oil and in oil production. Furthermore, we find that different geographical regions respond differently to oil supply shocks and oilspecific demand shocks that drive up oil prices, with Europe and North America being more negatively affected than emerging economies in Asia and South America. We demonstrate that this heterogeneity in responses is not only attributable to differences in energy intensity in production across regions but also to degree of openness and the investment share in GDP.

Traditional studies of the Dutch disease do not account for productivity spillovers between the booming resource sector and other domestic sectors. We put forward a simple theory model that allows for such spillovers. We then identify and quantify these spillovers using a Bayesian Dynamic Factor Model (BDFM). The model allows for resource movements and spending effects through a large panel of variables at the sectoral level, while also identifying disturbances to the commodity price, global demand and non-resource activity. Using Australia and Norway as representative cases studies, we find that a booming resource sector has substantial productivity spillovers on non-resource sectors, effects that have not been captured in previous analysis. That withstanding, there is also evidence of two-speed economies, with non-traded industries growing at a faster pace than traded. Furthermore, commodity prices also stimulate the economy, but primarily if an increase is caused by higher global demand. Commodity price growth unrelated to global activity is less favourable, and for Australia, there is evidence of a Dutch disease effect with crowding out of the tradable sectors. As such, our results show the importance of distinguishing between windfall gains due to volume and price changes when analysing the Dutch disease hypothesis.JEL-codes: C32, E32, F41, Q33