Leif Anders Thorsrud

Nowcasting GDP in Real Time: A Density Combination Approach

In this article, we use U.S. real-time data to produce combined density nowcasts of quarterly Gross Domestic Product (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.

What Drives Oil Prices? Emerging Versus Developed Economies

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.

Does forecast combination improve Norges Bank inflation forecasts

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.

Boom or gloom? Examining the Dutch disease in two-speed economies

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. The model allows for resource movements and spending effects through a large panel of variables, while also identifying disturbances to other shocks. 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.

Weights and Pools for a Norwegian Density Combination

We apply a suite of models to produce quasi-real-time density forecasts of Norwegian GDP and inflation, and evaluate different combination and selection methods using the Kullback-Leibler information criterion (KLIC). We use linear and logarithmic opinion pools in conjunction with various weighting schemes, and we compare these combinations to two different selection methods. In our application, logarithmic opinion pools were better than linear opinion pools, and score-based weights were generally superior to other weighting schemes. Model selection generally yielded poor density forecasts, as evaluated by KLIC.

The world is not enough! Small open economies and regional dependence

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.

Global and regional business cycles: Shocks and propagations

We study the synchronization of real and nominal variables across four different regions of the world, Asia, Europe, North and South America, covering 32 different countries. Employing a FAVAR framework, we distinguish between global and regional demand and supply shocks and document the relative contributions of these shocks to explaining macroeconomic fluctuations and synchronization. Our results support the decoupling hypothesis advanced in recent business cycle studies and yields new insights regarding the causes of business cycle synchronization. In particular, global supply shocks cause more severe activity fluctuations in European and North American economies than in Asian and South American economies, whereas global demand shocks shift activity in the different regions in opposite directions at longer horizons. Furthermore, demand shocks play a larger role than that found in related studies. Finally, only innovations to the Asian activity and price factors have significant spillover effects on shared global factors, demonstrating the growing importance of Asia in the global economy.

The World Is Not Enough! Small Open Economies and Regional Dependence

In this paper, we explicitly introduce regional factors into a global dynamic factor model. We combine new open economy factor models (emphasizing global shocks) with the recent findings of regional importance in the business cycle synchronization literature. The analysis is applied to a large panel of domestic data for four small open economies. We find that global and regional shocks explain roughly 30 and 20 percent, respectively, of the business cycle variation in all countries. While global shocks have most impact on trade variables, regional shocks explain a relatively large share of the variation in cost variables.

Commodity prices and fiscal policy design: Procyclical despite a rule

We analyse if the adoption of a fiscal rule insulates the domestic economy from commodity price fluctuations in a resource-rich economy. To do so we develop a timevarying Dynamic Factor Model, in which both the volatility of structural shocks and the systematic fiscal policy responses are allowed to change over time. We focus on a particular country, Norway, that is put forward as exemplary with its handling of resource wealth; income from the sale of petroleum is first saved in a sovereign wealth fund for then to be spent following a fiscal rule. We find that, contrary to common perception, fiscal policy has been more (not less) procyclical with commodity prices since the adoption of the rule. Fiscal policy has thereby exacerbated the commodity price fluctuations on the domestic economy. Still, compared to many other resource-rich economies practising a more spend-as-you-go strategy the responses are modest, as also documented in our counterfactual analysis. From a policy point of view, the implications of our findings are therefore of general interest since they highlight strengths and weaknesses of fiscal rules adopted in resource rich countries.

Forecasting GDP with Global Components. This Time is Different

We examine whether knowledge of in-sample co-movement across countries can be used in a more systematic way to improve forecast accuracy at the national level. In particular, we ask if a model with common international business cycle factors adds marginal predictive power compared to a domestic alternative? To answer this question we use a Dynamic Factor Model (DFM) and run an out-of-sample forecasting experiment. Our results show that exploiting the informational content in a common global business cycle factor improves forecast accuracy in terms of both point and density forecast evaluation across a large panel of countries. We also document that the Great Recession has a huge impact on this result, causing a clear preference shift towards the model including a common global factor. However, this time is different also in other respects. On longer forecasting horizons the performance of the DFM deteriorates substantially in the aftermath of the Great Recession.