We exploit unique Norwegian day-by-day transaction and hour-by-hour bidding logs data in order to examine how market participants reacted to the spreading news of Covid-19 in early March 2020, the lockdown on March 12, and the re-opening on April 20. We observe changes on the date of the lockdown in transaction volumes, sell-prediction spreads, exploitative bidding behavior, and seller confidence. However, when we compare observed price developments with our estimated counter-factual price developments, we find that about half of the total fall in prices had already occurred before the lockdown was implemented. The re-opening completely reverses the lockdown effect on prices. We show that voluntary behavioral changes, as well as lockdown and re-opening effects, are visible in various measures of social mobility, and that changes in daily news sentiment correlate with the abnormal price movements during this period.

Using a large news corpus and machine learning algorithms we investigate the role played by the media in the expectations formation process of households, and conclude that the news topics media report on are good predictors of both inflation and inflation expectations. In turn, in a noisy information model, augmented with a simple media channel, we document that the time series features of relevant topics help explain time-varying information rigidity among households. As such, we provide a novel estimate of state-dependent information rigidities and present new evidence highlighting the role of the media in understanding inflation expectations and information rigidities. (C) 2020 The Authors. Published by Elsevier B.V.

Using a unique dataset of 22.5 million news articles from the Dow Jones Newswires Archive, we perform an in depth real-time out-of-sample forecasting comparison study with one of the most widely used data sets in the newer forecasting literature, namely the FRED-MD dataset. Focusing on U.S. GDP, consumption and investment growth, our results suggest that the news data contains information not captured by the hard economic indicators, and that the news-based data are particularly informative for forecasting consumption developments.

We propose a simple method to quantify narratives from textual data, and identify "narrative monetary policy surprises" as the difference in narrative focus in central bank communication accompanying interest rate meetings and economic media coverage prior to those meetings. Identifying narrative surprises, using Norwegian data, provides surprise measures that are uncorrelated with conventional monetary policy surprises, and, in contrast to such surprises, have a significant effect on subsequent media coverage. Narrative monetary policy surprises lead to macroeconomic responses similar to what recent monetary policy literature associates with the information component of monetary policy communication, highlighting media's role as information intermediaries.

We exploit unique Norwegian day-by-day transaction and bid-by-bid auction data in order to examine how market participants reacted to the spreading news of Covid-19 in early March 2020, the lock-down on March 12, and the re-opening on April 20. We find that behavior changed voluntarily before the lock-down, and we find effects on the housing market from both the lock-down and the re-opening. In particular, there exists a discontinuity on the date of the lock-down in transaction volumes, sell-prediction spreads, aggressive bidding behavior, and seller confidence. However, when we compare observed price developments with our estimated counter-factual price developments, we find that roughly half of the total fall in prices had occurred when the lock-down was implemented. The re-opening completely reverses the lock-down effect on prices. We also show that voluntary behavioural changes, as well as lock-down and re-opening effects, are visible in various measures of social mobility, and that changes in daily news sentiment correlate with the abnormal price movements during this period.

Our analysis suggests; they do not! To arrive at this conclusion we construct a real-time data set of interest rate projections from central banks in three small open economies; New Zealand, Norway, and Sweden, and analyze if revisions to these projections (i.e., forward guidance) can be predicted by timely information. Doing so, we find a systematic role for forward looking international indicators in predicting the revisions to the interest rate projections in all countries. In contrast, using similar indexes for the domestic economy yields largely insignificant results. Furthermore, we find that revisions to forward guidance matter. Using a VAR identified with external instruments based on forecast errors from the predictive regressions, we show that the responses to output, inflation, the exchange rate and asset returns resemble those one typically associates with a conventional monetary policy shock.

We exploit unique Norwegian day-by-day transaction and bid-by-bid auction data in order to examine how market participants reacted to the spreading news of Covid-19 in early March 2020, the lock-down on March 12, and the re-opening on April 20. We nd that behavior changed voluntarily before the lock-down, and we nd e ects on the housing market from both the lock-down and the re-opening. In particular, there exists a discontinuity on the date of the lock-down in transaction volumes, sell-prediction spreads, aggressive bidding behavior, and seller con dence. However, when we compare observed price developments with our estimated counter-factual price developments, we nd that roughly half of the total fall in prices had occurred when the lock-down was implemented. The re-opening completely reverses the lock-down e ect on prices. We also show that voluntary behavioural changes, as well as lock-down and re-opening effects, are visible in various measures of social mobility, and that changes in daily news sentiment correlate with the abnormal price movements during this period.

The positive relationship between real exchange rates and natural resource income is well understood and studied. However, climate change and the transition to a lower-carbon economy now challenges this relationship. We document this by proposing a novel news media-based measure of climate change transition risk and show that when such risk is high, major commodity currencies experience a persistent depreciation and the relationship between commodity price fluctuations and currencies tends to become weaker.

Economic RecordVolume 96, Issue 315 p. 526-527 Review Forecasting: An Essential Introduction, by Jennifer Castle, Michael Clements and David Hendry ( Yale University Press, New Haven and London, 2019), pp. 240. Leif Anders Thorsrud, Leif Anders Thorsrud BI Norwegian Business School, Oslo, NorwaySearch for more papers by this author Leif Anders Thorsrud, Leif Anders Thorsrud BI Norwegian Business School, Oslo, NorwaySearch for more papers by this author First published: 23 December 2020 https://doi.org/10.1111/1475-4932.12588Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume96, Issue315December 2020Pages 526-527 RelatedInformation

I construct a daily business cycle index based on quarterly GDP growth and textual information contained in a daily business newspaper. The newspaper data are decomposed into time series representing news topics, while the business cycle index is estimated using the 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 classifies the phases of the business cycle with almost perfect accuracy and provides broad-based high-frequency information about the type of news that drive or reflect economic fluctuations. In out-of-sample nowcasting experiments, the model is competitive with forecast combination systems and expert judgment, and produces forecasts with predictive power for future revisions in GDP. Thus, news reduces noise. Supplementary materials for this article are available online.

Using a large news corpus and machine learning algorithms we investigate the role played by the media in the expectations formation process of households, and conclude that the news topics media report on are good predictors of both inflation and inflation expectations. In turn, in a noisy information model, augmented with a simple media channel, we document that the time series features of relevant topics help explain the time-varying information rigidity among households. As such, we provide a novel estimate of state dependent information rigidities, and present new evidence highlighting media’s role for understanding inflation expectations and information rigidities. JEL-codes: C11, C53, D83, D84, E13, E31, E37

In this paper we develop the first model to incorporate the dynamic productivity consequences of both the spending effect and the resource movement effect of oil abundance. We show that doing so dramatically alters the conclusions drawn from earlier models of learning by doing (LBD) and the Dutch disease. In particular, the resource movement effect suggests that the growth effects of natural resources are likely to be positive, turning previous growth results in the literature relying on the spending effect on their head. We motivate the relevance of our approach by the example of a major oil producer, Norway. Empirically we find that the effects of an increase in the price of oil may resemble results found in the earlier Dutch disease literature, while the effects of increased oil activity increases productivity in most industries. Therefore, models that only focus on windfall gains due to increased spending potential from higher oil prices, would conclude – incorrectly based on our analysis – that the resource sector cannot be an engine of growth.

We decompose the textual data in a Norwegian business newspaper into news topics and investigate their role in predicting and explaining economic fluctuations. Separate full- and out-of-sample experiments show that many topics have predictive power for key economic variables, including asset prices. Unexpected innovations to an aggregated news index, derived as a weighted average of the topics with the highest predictive scores, lead to persistent economic fluctuations, and are especially associated with financial markets, credit and borrowing. Unexpected innovations to asset prices, orthogonal to news shocks and labeled as noise, have only temporary positive effects, in line with economic theory.

In this paper we develop the first model to incorporate the dynamic productivity consequences of both the spending effect and the resource movement effect of oil abundance. We show that doing so dramatically alters the conclusions drawn from earlier models of learning by doing (LBD) and the Dutch disease. In particular, the resource movement effect suggests that the growth effects of natural resources are likely to be positive, turning previous growth results in the literature relying on the spending effect on their head. We motivate the relevance of our approach by the example of a major oil producer, Norway. Empirically we find that the effects of an increase in the price of oil may resemble results found in the earlier Dutch disease literature, while the effects of increased oil activity increases productivity in most industries. Therefore, models that only focus on windfall gains due to increased spending potential from higher oil prices, would conclude incorrectly based on our analysis that the resource sector cannot be an engine of growth.

In this paper we develop the first model to incorporate the dynamic productivity consequences of both the spending effect and the resource movement effect of oil abundance. We show that doing so dramatically alters the conclusions drawn from earlier models of learning by doing (LBD) and the Dutch disease. In particular, the resource movement effect suggests that the growth effects of natural resources are likely to be positive, turning previous growth results in the literature relying on the spending effect on their head. We motivate the relevance of our approach by the example of a major oil producer, Norway, where it seems clear that the predictions based on existing theory do not apply. Although the effects of an increase in the price of oil may resemble results found in the earlier Dutch disease literature, the effects of increased oil activity do not. Therefore, models that only focus on windfall gains due to increased spending potential from higher oil prices, would conclude incorrectly based on our analysis that the resource sector cannot be an engine of

Research about narratives’ role in economics is scarce, while real word experience and research in other sciences suggest they matter a lot. This article proposes a view and methodology for quantifying the epidemiology of media narratives relevant to business cycles in the US, Japan, and Europe. We do so by first constructing quantitative measures of narratives based on the news topics the media writes about. We then estimate daily business cycle indexes using this type of data, derive virality indexes capturing the extent to which narratives relevant for business cycles go viral, and finally use so called “Graphical Granger causality” modeling to cast light on cross-country spillovers and whether or not narratives carry news or noise. Our results highlight the informativeness of narratives for describing economic fluctuations, have a clear practical relevance for high-frequency business cycle monitoring, and suggest that narratives capture more than the market’s animal spirits.

We decompose the textual data in a daily Norwegian business newspaper into news topics and investigate their predictive and causal role for asset prices. Our three main findings are: (1) a one unit innovation in the news topics predict roughly a 1 percentage point increase in close-to-open returns and significant continuation patterns peaking at 4 percentage points after 15 business days, with little sign of reversal; (2) simple zero-cost news-based investment strategies yield significant annualized risk-adjusted returns of up to 20 percent; and (3) during a media shortage, due to an exogenous strike, returns for firms particularly exposed to our news measure experience a substantial fall. Our estimates suggest that between 20 to 40 percent of the news topics’ predictive power is due to the causal media effect. Together these findings lend strong support for a rational attention view where the media alleviate information frictions and disseminate fundamental information to a large population of investors. JEL-codes: C5, C8, G4, G12

We decompose the textual data in a daily Norwegian business newspaper into news topics and investigate their predictive and causal role for asset prices. Our three main findings are: (1) a one unit innovation in the news topics predict roughly a 1 percentage point increase in close-to-open returns and significant continuation patterns peaking at 4 percentage points after 15 business days, with little sign of reversal; (2) simple zero-cost news-based investment strategies yield significant annualized risk-adjusted returns of up to 20 percent; and (3) during a media shortage, due to an exogenous strike, returns for firms particularly exposed to our news measure experience a substantial fall. Our estimates suggest that between 20 to 40 percent of the news topics' predictive power is due to the causal media effect. Together these findings lend strong support for a rational attention view where the media alleviate information frictions and disseminate fundamental information to a large population of investors.

We examine whether a knowledge of in-sample co-movement across countries can be used in a more systematic way to improve the forecast accuracy at the national level. In particular, we ask whether a model with common international business cycle factors adds marginal predictive power over a domestic alternative. We answer this question using 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 the forecast accuracy in terms of both point and density forecast evaluation across a large panel of countries. We also document evidence showing that the Great Recession has a huge impact on this result, causing a clear shift in preferences towards the model that includes a common global factor. However, this time is different in other respects too, as the performance of the DFM deteriorates substantially for longer forecasting horizons in the aftermath of the Great Recession.