The ‘Dutch disease’ reexamined: Resource booms can benefit the wider economy

In the cases of Norway and Australia, there's evidence of productivity 'spillovers' between industries, write Hilde C. Bjørnland and Leif Anders Thorsru

Nowcasting Using News Topics. Big Data Versus Big Bank

The agents in the economy use a plethora of high frequency information, including news media, to guide their actions and thereby shape aggregate economic fluctuations. Traditional nowcasting approches have to a relatively little degree made use of such information. In this paper, I show how unstructured textual information in a business newspaper can be decomposed into daily news topics and used to nowcast quarterly GDP growth. Compared with a big bank of experts, here represented by official central bank nowcasts and a state-of-the-art forecast combination system, the proposed methodology performs at times up to 15 percent better, and is especially competitive around important business cycle turning points. Moreover, if the statistical agency producing the GDP statistics itself had used the news-based methodology, it would have resulted in a less noisy revision process. Thus, news reduces noise.publishedVersio

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.publishedVersio

International business cycles and oil market dynamics

This dissertation is about understanding business cycle fluctuations in an international perspective. What causes them, and how do they transmit across borders? In essence the motivation for the dissertation can be summarized with two words, globalization and regionalism. The dissertation presents a series of four papers. Three common features are representative for these papers: They are all empirical investigations of, in general international and in particular domestic, business cycle fluctuations. The methodological framework used for inference utilizes large data sets, combining both the time and cross sectional dimension of the information set available. Lastly, all the papers build on the findings in the business cycle literature of a high degree of cross country synchronization, but extend this line of research by identifying common shocks. A prime candidate for a shock that is truly common across borders is unexpected disturbances to the price of oil. Two of the papers in the dissertation explicitly explore the linkages between the global oil market and the macro economy. Furthermore, just as individual countries are highly interconnected so are likely also the sectors of a given economy. Thus, to fully understand how international shocks like, e.g., unexpected innovations in the real price of oil, transmit to domestic economies, production networks, trade between sectors and potentially shared productivity dynamics between the different sectors in the economy might all be important. The last paper in this dissertation addresses this issue

Words Are the New Numbers: A Newsy Coincident Index of Business Cycles

I construct a daily business cycle index based on quarterly GDP and textual information contained in a daily business newspaper. The newspaper data are decomposed into time series representing newspaper topics using a Latent Dirichlet Allocation model. The business cycle index is estimated using the newspaper topics and a time-varying Dynamic Factor Model where dynamic sparsity is enforced upon the factor loadings using a latent threshold mechanism. The resulting index is shown to be not only more timely but also more accurate than commonly used alternative business cycle indicators. Moreover, the derived index provides the index user with broad based high frequent information about the type of news that drive or re ect economic uctuations.publishedVersio

Forecasting inflation in real-time

Summary Flexible inflation targeting has become the preferred policy among a growing number of central banks over the last decades. Due to the lag between interest rates and inflation, optimal monetary policy in this framework is essentially about forecasting inflation (Svensson and Woodford, 2003). The output gap, measuring the deviation of output from potential, has a key role in this regard. Through different transition mechanisms a positive output gap leads to inflation. For central banks aiming at a flexible inflation target, an appropriate policy response to the observed pressure in the economy will not only help stabilize inflation at a desired level, but also stabilize output (Svensson, 1997 and 2000). If the policy reactions are going to be proper, the measure of the output gap has to be adequate. As demonstrated in this and other analysis it seldom is (see for example Orphanides and van Norden (2002) and Bernhardsen, Eitrheim, Jore and Røisland (2004). There are basically two factors making the derivation of the output gap difficult. The first concerns the estimation procedure. Since one fails to reject the hypothesis of a unit root in macroeconomic time series, the long run trend of output can no longer be treated as deterministic; see e.g. Nelson and Plosser (1982). Accordingly, the computation of potential output has to take into consideration the estimation of a stochastic trend, which greatly complicates the measuring of potential output and the output gap. The second factor concerns the real-time nature at which central banks have to conduct monetary policy: Decisions are based on highly uncertain data, which are subjected to substantial revisions. This is especially true of the output. There are three main reasons for changes to official statistics. • The earliest estimates are based on preliminary and incomplete information. • Changes to the base year. • The national accounts are occasionally subject to major revisions. Real-time data is data as it was observed at each point in time, and typically categorized into different vintages describing their time of release, thus taking into account these data revision processes. In the spirit of Orphanides and van Norden (2005), this paper examines two different methods for extracting the output gap in real-time, and evaluates their performance in forecasting Norwegian inflation. Especially, I question whether the inclusion of the output gap gives any value added in forecasting Norwegian domestic inflation compared to simple autoregressive benchmark models. The answer clearly depends on factors as model specifications, evaluation criteria, the forecasting periods and the quality of the data: The output gap models evaluated are the Hodrick-Prescott filter and the Production function method. As a benchmark forecasting model I employ a linear AR(p) model of inflation. My main forecasting model is a Phillips curve relation including the output gap. These specifications make it possible to relate inflation to real activity. I have used root mean square forecast errors (RMSFE) to assess the forecasting performance, and the forecasting period has ranged from 94q1 to 06q2. By using real-time data this paper highlights the problems and the uncertainties brought forward by the data revision processes. To my knowledge real-time forecasting exercises of this kind has not been conducted on Norwegian data before. Bjørnland, Brubakk and Jore (2007) found that models including the output gap gave a better predictive power of inflation than models based on alternative indicators, and that they forecasted significantly better than simple benchmark models, but they did not use real-time data. Based on real-time data estimations my findings suggests that the inclusion of the output gap makes the out-of-sample forecasts less accurate than what would have been attained if the simpler benchmark models had been used, a finding that is consistent with results reported in Orphanides and van Norden (2005). Some output gap models computed in real-time do however forecast better than the benchmark models, but the results seems to be very sensitive to the chosen forecasting period. Further I find that there are considerable differences in forecasting performance between using real-time data, and final vintage data (the final vintage in the sample has been 06q2). The reminder of this paper is organized as follows: Section 2 describes the output gap concept, the output gap models and the real-time data sets that I have used. Sections 2-2.2 follow Bjørnland, Brubakk and Jore (2004), and Frøyland and Nymoen (2000) closely. For a more thorough exposition of the output gap, and the different methods to extract it, I refer to the cited papers. Section 2.4 illustrates clearly how the real-time issues affect the output gap estimates. Section 3 presents the forecasting methodology. Sections 4 and 5 present the results and conclusions. I have used Matlab computer software and the Econometrics Toolbox provided by James P. LeSage for my computations. Programming codes can be made available on request

Words Are the New Numbers: A Newsy Coincident Index of Business Cycles

I construct a daily business cycle index based on quarterly GDP and textual information contained in a daily business newspaper. The newspaper data are decomposed into time series representing newspaper topics using a Latent Dirichlet Allocation model. The business cycle index is estimated using the newspaper topics and a time-varying Dynamic Factor Model where dynamic sparsity is enforced upon the factor loadings using a latent threshold mechanism. The resulting index is shown to be not only more timely but also more accurate than commonly used alternative business cycle indicators. Moreover, the derived index provides the index user with broad based high frequent information about the type of news that drive or re ect economic uctuations

Commodity prices and fiscal policy design: Procyclical despite a rule

We analyse if the adoption of a fiscal spending rule insulates the domestic economy from commodity price fluctuations in a resource-rich economy. To do so we develop a time-varying Dynamic Factor Model, in which we allow both the volatility of structural shocks and the systematic fiscal policy responses to change over time. We focus on Norway, a country that is put forward as exemplary with its handling of resource wealth. Unlike most oil exporters, Norway has devised a fiscal frame- work with the view to shield the domestic economy from oil price fluctuations. By transferring its petroleum revenues to a sovereign wealth fund, and then consuming only the expected real return on the fund, fiscal policy allows for a gradual phasing in of the petroleum revenue, unrelated to movements in oil prices. We find that, contrary to common perception, fiscal policy has been more (not less) procyclical with commodity prices since the adoption of the fiscal rule in 2001. Fiscal policy has thereby worked to exacerbate the commodity price fluctuations on the domestic economy. Large inflows of money to the fund during a period of rapidly increasing oil prices is part of the explanation. Still, Norway has managed to save a large share of its petroleum income for future generations. Compared to many other resource-rich economies practising a more spend-as-you-go strategy, this is a great success

Nowcasting Using News Topics. Big Data Versus Big Bank

The agents in the economy use a plethora of high frequency information, including news media, to guide their actions and thereby shape aggregate economic fluctuations. Traditional nowcasting approches have to a relatively little degree made use of such information. In this paper, I show how unstructured textual information in a business newspaper can be decomposed into daily news topics and used to nowcast quarterly GDP growth. Compared with a big bank of experts, here represented by official central bank nowcasts and a state-of-the-art forecast combination system, the proposed methodology performs at times up to 15 percent better, and is especially competitive around important business cycle turning points. Moreover, if the statistical agency producing the GDP statistics itself had used the news-based methodology, it would have resulted in a less noisy revision process. Thus, news reduces noise

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