Are oil price forecasters finally right? Regressive expectations toward more fundamental values of the oil price

We use oil price forecasts from the Consensus Economic Forecast poll to analyze how forecasters form their expectations. Our findings seem to indicate that the extrapolative as well as the regressive expectation formation hypothesis play a role. Standard measures of forecast accuracy reveal forecasters' underperformance relative to the random walk benchmark. However, this result appears to be biased due to peso problems. --Oil price,survey data,forecast bias,peso problem

Nonlinear expectations in speculative markets - Evidence from the ECB survey of professional forecasters

Chartist and fundamentalist models have proven to be capable of replicating stylized facts on speculative markets. In general, this is achieved by specifying nonlinear interactions of otherwise linear asset price expectations of the respective trader groups. This paper investigates whether or not regressive and extrapolative expectations themselves exhibit significant nonlinear dynamics. The empirical results are based on a new data set from the European Central Bank Survey of Professional Forecasters on oil price expectations. In particular, we find that forecasters form destabilizing expectations in the neighborhood of the fundamental value, whereas expectations tend to be stabilizing in the presence of substantial oil price misalignment.Agent based models; nonlinear expectations; survey data

Nonlinear Expectation Formation in the U.S. Stock Market

This research applies data from the Livingston survey to study the time variation in the sentiment of U.S. stock-market forecasters. A Panel Smooth Transition Regression (STR) model is estimated to identify the importance of market conditions summarized by stock-market misalignments and recent returns for the formation of regressive and extrapolative expectations. We find that survey participants expect little mean reversion in times of large misalignments reflecting the observed substantial and persistent swings in stock prices. Recent returns are negatively extrapolated depending on the sign and the size of the return revealing a contrarian behavior of forecasters in the presence of market exuberance

Heterogeneous forecasters and nonlinear expectation formation in the US stock market

We use a Panel Smooth Transition Regression (STR) model to study nonlinearities in the expectation-formation process in the U.S. stock market. To this end, we use data from the Livingston survey to investigate how the importance of regressive and extrapolative expectations fluctuates over time as market conditions summarized by stock-market misalignments and recent returns change. We find that survey participants form stabilizing expectations in the long run. Short-run expectations, in contrast, are consistent with weak mean reversion of stock prices

Heterogeneous Forecasters and Nonlinear Expectation Formation in the U.S. Stock Market

We use a Panel Smooth Transition Regression (STR) model to study nonlinearities in the expectation-formation process in the U.S. stock market. To this end, we use data from the Livingston survey to investigate how the importance of regressive and extrapolative expectations fluctuates over time as market conditions summarized by stock-market misalignments and recent returns change. We find that survey participants form stabilizing expectations in the long run. Short-run expectations, in contrast, are consistent with weak mean reversion of stock prices

Data sets for author name disambiguation: an empirical analysis and a new resource

Data sets of publication meta data with manually disambiguated author names play an important role in current author name disambiguation (AND) research. We review the most important data sets used so far, and compare their respective advantages and shortcomings. From the results of this review, we derive a set of general requirements to future AND data sets. These include both trivial requirements, like absence of errors and preservation of author order, and more substantial ones, like full disambiguation and adequate representation of publications with a small number of authors and highly variable author names. On the basis of these requirements, we create and make publicly available a new AND data set, SCAD-zbMATH. Both the quantitative analysis of this data set and the results of our initial AND experiments with a naive baseline algorithm show the SCAD-zbMATH data set to be considerably different from existing ones. We consider it a useful new resource that will challenge the state of the art in AND and benefit the AND research community

Nonlinear expectation formation in the U.S. stock market: Empirical evidence from the Livingston survey

We use a Panel Smooth Transition Regression (STR) model to study nonlinearities in the expectation-formation process in the U.S. stock market. To this end, we use data from the Livingston survey to investigate how the importance of regressive and extrapolative expectations fluctuates over time as market conditions summarized by stock-market misalignments and recent returns change. We find that survey participants form stabilizing expectations in the long run. Short-run expectations, in contrast, are consistent with weak mean reversion of stock prices

Heteroeneous forecasters and nonlinear expectation formation in US stock market

We use a Panel Smooth Transition Regression (STR) model to study nonlinearities in the expectationformation process in the US stock market. To this end, we use data from the Livingston survey to investigate how the importance of regressive and extrapolative expectations fluctuates over time as market conditions summarized by stock-market misalignments and recent returns change. We find that survey participants form stabilizing expectations in the long run. Short-run expectations, in contrast, are consistent with weak mean reversion of stock prices

Nonlinear expectations in speculative markets: Evidence from the ECB survey of professional forecasters

Chartist and fundamentalist models have proven to be capable of replicating stylized facts on speculative markets. In general, this is achieved by specifying nonlinear interactions of otherwise linear asset price expectations of the respective trader groups. This paper investigates whether or not regressive and extrapolative expectations themselves exhibit significant nonlinear dynamics. The empirical results are based on a new data set from the European Central Bank Survey of Professional Forecasters on oil price expectations. In particular, we find that forecasters form destabilizing expectations in the neighborhood of the fundamental value, whereas expectations tend to be stabilizing in the presence of substantial oil price misalignment. --agent based models,nonlinear expectations,survey data

AtlantOS Data Management Plan Framework

Version No.:1.2. -Implementation of AtlantOS Catalogue and GEOSS requirement