Forecasting Natural Gas Prices in Real Time
Christiane Baumeister, Florian Huber, Thomas K. Lee, Francesco Ravazzolo
NBER Working Paper,
No. 33156,
2024
Abstract
This paper provides a comprehensive analysis of the forecastability of the real price of natural gas in the United States at the monthly frequency considering a universe of models that differ in their complexity and economic content. Our key finding is that considerable reductions in mean-squared prediction error relative to a random walk benchmark can be achieved in real time for forecast horizons of up to two years. A particularly promising model is a six-variable Bayesian vector autoregressive model that includes the fundamental determinants of the supply and demand for natural gas. To capture real-time data constraints of these and other predictor variables, we assemble a rich database of historical vintages from multiple sources. We also compare our model-based forecasts to readily available model-free forecasts provided by experts and futures markets. Given that no single forecasting method dominates all others, we explore the usefulness of pooling forecasts and find that combining forecasts from individual models selected in real time based on their most recent performance delivers the most accurate forecasts.
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8th Round [DL FILTER]
8th Round Welcome to the CompNet dataset. In this page, you can navigate through the many files that compose the dataset and choose which one you need for your analysis. Below,…
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Transformation tables for administrative borders in Germany
Transformation tables for administrative borders in Germany The state has the ability to change the original spatial structure of its administrative regions. The stated goal of…
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Exploring Accounting Research Topic Evolution: An Unsupervised Machine Learning Approach
June Cao, Zhanzhong Gu, Iftekhar Hasan
Journal of International Accounting Research,
No. 3,
2023
Abstract
This study explores the evolution of accounting research by utilizing an unsupervised machine learning approach. We aim to identify the latent topics of accounting from the 1980s up to 2018, the dynamics and emerging topics of accounting research, and the economic reasons behind those changes. First, based on 23,220 articles from 46 accounting journals, we identify 55 topics using the latent Dirichlet allocation model. To illustrate the connection between topics, we use HistCite to generate a citation map along a timeline. The citation clusters demonstrate the “tribalism” phenomenon in accounting research. We then implement the dynamic topic model to reveal the dynamics of topics to show changes in accounting research. The emerging research trends are identified from the topic analytics. We further explore the economic reasons and in-depth insights into the topic evolution, indicating the economic development embeddedness nature of accounting research.
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EVA-KULT
EVA-KULT Establishing Evidence-based Evaluation Methods for Subsidy Programmes in Germany The project aims at expanding the Centre for Evidence-based Policy Advice at the Halle…
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Anniversary 2017
Anniversary 2017 25 years IWH. Highlights and turning points. A quarter of a century ago, our employees started to work for IWH. What has happened from then until now? Content 25…
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Privacy
Data Protection Policy We take the protection of your personal data very seriously and treat your personal data with confidentiality and in compliance with the provisions of law…
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Predicting Free-riding in a Public Goods Game – Analysis of Content and Dynamic Facial Expressions in Face-to-Face Communication
Dmitri Bershadskyy, Ehsan Othman, Frerk Saxen
IWH Discussion Papers,
No. 9,
2019
Abstract
This paper illustrates how audio-visual data from pre-play face-to-face communication can be used to identify groups which contain free-riders in a public goods experiment. It focuses on two channels over which face-to-face communication influences contributions to a public good. Firstly, the contents of the face-to-face communication are investigated by categorising specific strategic information and using simple meta-data. Secondly, a machine-learning approach to analyse facial expressions of the subjects during their communications is implemented. These approaches constitute the first of their kind, analysing content and facial expressions in face-to-face communication aiming to predict the behaviour of the subjects in a public goods game. The analysis shows that verbally mentioning to fully contribute to the public good until the very end and communicating through facial clues reduce the commonly observed end-game behaviour. The length of the face-to-face communication quantified in number of words is further a good measure to predict cooperation behaviour towards the end of the game. The obtained findings provide first insights how a priori available information can be utilised to predict free-riding behaviour in public goods games.
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An Empirical Analysis of Legal Insider Trading in The Netherlands
Frank de Jong, Jérémie Lefebvre, Hans Degryse
De Economist,
No. 1,
2014
Abstract
In this paper, we employ a registry of legal insider trading for Dutch listed firms to investigate the information content of trades by corporate insiders. Using a standard event-study methodology, we examine short-term stock price behavior around trades. We find that purchases are followed by economically large abnormal returns. This result is strongest for purchases by top executives and for small market capitalization firms, which is consistent with the hypothesis that legal insider trading is an important channel through which information flows to the market. We analyze also the impact of the implementation of the Market Abuse Directive (European Union Directive 2003/6/EC), which strengthens the existing regulation in the Netherlands. We show that the new regulation reduced the information content of sales by top executives.
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