Leif Anders Thorsrud

CME Working Paper

Norsk økonomi går godt. En viktig grunn til dette er petroleumsnæringens bidrag til økt produktivitet i resten av økonomien. Men flere varsellamper blinker. Den senere tid har produktivitetsveksten vært avtagende. Høyere oljepriser har ført til økte lønnskostnader, som igjen har svekket konkurranseevnen. Det er ikke sikkert at vi kan regne med like gunstig utvikling det neste tiåret. Trolig er det først da vi virkelig får en test på om vi er immune mot Hollandsk syke.

Handelshøyskolen BI

Traditional studies of the Dutch disease do not typically account for productiv- ity spillovers between the booming energy sector and non-oil sectors. This study identifies and quantifies 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 real oil price, global demand and non-oil activity. Using Norway as a repre- sentative case study, we find that a booming energy sector has substantial spillover effects on the non-oil sectors. Furthermore, windfall gains due to changes in the real oil price also stimulates the economy, but primarily if the oil price increase is caused by global demand. Oil price increases due to, say, supply disruptions, while stimulating activity in the technologically intense service sectors and boosting gov- ernment spending, have small spillover effects on the rest of the economy, primarily because of reduced cost competitiveness. Yet, there is no evidence of Dutch disease. Instead, we find evidence of a two-speed economy, with non-tradables growing at a much faster pace than tradables. Our results suggest that traditional Dutch dis- ease models with a fixed capital stock and exogenous labor supply do not provide a convincing explanation for how petroleum wealth affects a resource rich economy when there are productivity spillovers between sectors.

EEA I ESEM 2012

6th CSDA International Conference on Computational and Financial Econometrics (CFE 2012)

Handelshøyskolen BI

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 oil- specific 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.

Handelshøyskolen BI

This paper bridges the new open economy factor augmented VAR (FAVAR) studies with the recent findings in the business cycle synchronization literature emphasizing the importance of regional factors. That is, we estimate and identify a three block FAVAR model with separate world, regional and domestic blocks and study the transmission of both global and regional shocks to four small open economies (Canada, New Zealand, Norway and UK). The results show that foreign shocks explain a major share of the variance in all countries, most so shocks that are common to the world. However, regional shocks also play an important role, explaining more than 20 percent of the variance in the variables. Hence in small open economies, the world is not enough. The regional factors impact the four countries differently, though, some through trade and some through consumer sentiment. Our findings of a strong transmission of both global and regional shocks to open economies are in sharp contrast to the evidence from recently developed open economy DSGE models.

72nd International Atlantic Economic Conference

Norges Bank

Handelshøyskolen BI

In this paper we use U.S. real-time vintage data and produce combined density nowcasts for quarterly GDP growth from a system of three commonly used model classes. The density nowcasts are combined in two steps. First, a wide selection of individual models within each model class are combined separately. Then, the nowcasts from the three model classes are combined into a single predictive density. We update the density nowcast for every new data release throughout the quarter, and highlight the importance of new information for the evaluation period 1990Q2-2010Q3. Our results show that the logarithmic score of the predictive densities for U.S. GDP increase almost monotonically as new information arrives during the quarter. While the best performing model class is changing during the quarter, the density nowcasts from our combination framework is always performing well both in terms of 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.

Reserve Bank of New Zealand conference: "The transmission of international shocks to open economies"

Handelshøyskolen BI

We develop a system that provides model-based forecasts for inflation in Norway. We recursively evaluate quasi out-of-sample forecasts from a large suite of models from 1999 to 2009. The performance of the models are then used to derive quasi real time weights that are used to combine the forecasts. Our results indicate that a combination forecast improves upon the point forecasts from individual models. Furthermore, a combination forecast out-performs Norges Bank's own point forecast for inflation. The beneficial results are obtained using a trimmed weighted average. Some degree of trimming is required for the combination forecasts to out-perform the judgmental forecasts from the policymaker.

16th International Conference on Computing in Economics and Finance