Consensus Forecast
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Consensus Forecast
Used in a number of sciences, ranging from econometrics to meteorology, consensus forecasts are predictions of the future that are created by combining together several separate forecasts which have often been created using different methodologies. Also known as ''combining forecasts'', ''forecast averaging'' or ''model averaging'' (in econometrics and statistics) and ''committee machines'', ''ensemble averaging'' or ''expert aggregation'' (in machine learning). Applications can range from forecasting the weather to predicting the annual Gross Domestic Product of a country or the number of cars a company or an individual dealer is likely to sell in a year. While forecasts are often made for future values of a time series, they can also be for one-off events such as the outcome of a presidential election or a football match. Background Forecasting plays a key role in any organisation's planning process as it provides insight into uncertainty. Through simulation, one will be able to ...
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Econometrics
Econometrics is the application of Statistics, statistical methods to economic data in order to give Empirical evidence, empirical content to economic relationships.M. Hashem Pesaran (1987). "Econometrics," ''The New Palgrave: A Dictionary of Economics'', v. 2, p. 8 [pp. 8–22]. Reprinted in J. Eatwell ''et al.'', eds. (1990). ''Econometrics: The New Palgrave''p. 1[pp. 1–34].Abstract (The New Palgrave Dictionary of Economics, 2008 revision by J. Geweke, J. Horowitz, and H. P. Pesaran). More precisely, it is "the quantitative analysis of actual economic Phenomenon, phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference". An introductory economics textbook describes econometrics as allowing economists "to sift through mountains of data to extract simple relationships". Jan Tinbergen is one of the two founding fathers of econometrics. The other, Ragnar Frisch, also coined the term in the sense in which it is used toda ...
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Organisation For Economic Co-operation And Development
The Organisation for Economic Co-operation and Development (OECD; french: Organisation de coopération et de développement économiques, ''OCDE'') is an intergovernmental organization, intergovernmental organisation with 38 member countries, founded in 1961 to stimulate economic progress and world trade. It is a Forum (legal), forum whose member countries describe themselves as committed to democracy and the market economy, providing a platform to compare policy experiences, seek answers to common problems, identify good practices, and coordinate domestic and international policies of its members. The majority of OECD members are High income economy, high-income economies with a very high Human Development Index, Human Development Index (HDI), and are regarded as Developed country, developed countries. Their collective population is 1.38 billion. , the OECD member countries collectively comprised 62.2% of List of countries by GDP (nominal), global nominal GDP (US$49.6 trill ...
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Futures Techniques
Futures techniques used in the multi-disciplinary field of futurology by futurists in Americas and Australasia, and futurology by futurologists in EU, include a diverse range of forecasting methods, including anticipatory thinking, backcasting, simulation, and visioning. Some of the anticipatory methods include, the delphi method, causal layered analysis, environmental scanning, morphological analysis, and scenario planning. Anticipatory thinking protocols Delphi method The Delphi method is a popular technique used in futurology. It was developed by Gordon and Helmer in 1953 at RAND. It can be defined as a method for structuring a group communication process, so that the process is effective in allowing a group of individuals, as a whole, to deal with a complex problem. It uses the iterative, independent questioning of a panel of experts to assess the timing, probability, significance and implications of factors, trends and events in the relation to the problem being cons ...
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Reference Class Forecasting
Reference class forecasting or comparison class forecasting is a method of predicting the future by looking at similar past situations and their outcomes. The theories behind reference class forecasting were developed by Daniel Kahneman and Amos Tversky. The theoretical work helped Kahneman win the Nobel Prize in Economics. Reference class forecasting is so named as it predicts the outcome of a planned action based on actual outcomes in a reference class of similar actions to that being forecast. Discussion of which reference class to use when forecasting a given situation is known as the reference class problem. Overview Kahneman and Tversky Decision Research Technical Report PTR-1042-77-6. In found that human judgment is generally optimistic due to overconfidence and insufficient consideration of distributional information about outcomes. People tend to underestimate the costs, completion times, and risks of planned actions, whereas they tend to overestimate the benefits of ...
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Ensemble Forecasting
Ensemble forecasting is a method used in or within numerical weather prediction. Instead of making a single forecast of the most likely weather, a set (or ensemble) of forecasts is produced. This set of forecasts aims to give an indication of the range of possible future states of the atmosphere. Ensemble forecasting is a form of Monte Carlo method, Monte Carlo analysis. The multiple simulations are conducted to account for the two usual sources of uncertainty in forecast models: (1) the errors introduced by the use of imperfect initial conditions, amplified by the Chaos theory, chaotic nature of the evolution equations of the atmosphere, which is often referred to as sensitive dependence on initial conditions; and (2) errors introduced because of imperfections in the model formulation, such as the approximate mathematical methods to solve the equations. Ideally, the verified future atmospheric state should fall within the predicted ensemble Standard deviation, spread, and the amou ...
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Ensemble Averaging
In machine learning, particularly in the creation of artificial neural networks, ensemble averaging is the process of creating multiple models and combining them to produce a desired output, as opposed to creating just one model. Frequently an ensemble of models performs better than any individual model, because the various errors of the models "average out." Overview Ensemble averaging is one of the simplest types of committee machines. Along with boosting, it is one of the two major types of static committee machines.Haykin, Simon. Neural networks : a comprehensive foundation. 2nd ed. Upper Saddle River N.J.: Prentice Hall, 1999. In contrast to standard network design in which many networks are generated but only one is kept, ensemble averaging keeps the less satisfactory networks around, but with less weight.Hashem, S. "Optimal linear combinations of neural networks." Neural Networks 10, no. 4 (1997): 599–614. The theory of ensemble averaging relies on two properties o ...
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Economic Forecasting
Economic forecasting is the process of making predictions about the economy. Forecasts can be carried out at a high level of aggregation—for example for GDP, inflation, unemployment or the fiscal deficit—or at a more disaggregated level, for specific sectors of the economy or even specific firms. Economic forecasting is a measure to find out the future prosperity of a pattern of investment and is the key activity in economic analysis. Many institutions engage in economic forecasting: national governments, banks and central banks, consultants and private sector entities such as think-tanks, companies and international organizations such as the International Monetary Fund, World Bank and the OECD. A broad range of forecasts are collected and compiled b"Consensus Economics" Some forecasts are produced annually, but many are updated more frequently. The economist typically considers risks (i.e., events or conditions that can cause the result to vary from their initial estimates). ...
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Delphi Method
} The Delphi method or Delphi technique ( ; also known as Estimate-Talk-Estimate or ETE) is a structured communication technique or method, originally developed as a systematic, interactive forecasting method which relies on a panel of experts. The technique can also be adapted for use in face-to-face meetings, and is then called mini-Delphi. Delphi has been widely used for business forecasting and has certain advantages over another structured forecasting approach, prediction markets. Delphi is based on the principle that forecasts (or decisions) from a structured group of individuals are more accurate than those from unstructured groups. The experts answer questionnaires in two or more rounds. After each round, a facilitator or change agent provides an anonymised summary of the experts' forecasts from the previous round as well as the reasons they provided for their judgments. Thus, experts are encouraged to revise their earlier answers in light of the replies of other members of ...
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Consensus Decision-making
Consensus decision-making or consensus process (often abbreviated to ''consensus'') are group decision-making processes in which participants develop and decide on proposals with the aim, or requirement, of acceptance by all. The focus on establishing agreement of at least the majority or the supermajority and avoiding unproductive opinion differentiates consensus from unanimity, which requires all participants to support a decision. Origin and meaning of terms The word ''consensus'' is Latin meaning "agreement, accord", derived from ''consentire'' meaning "feel together". Broadly, ''consensus'' relates to a generally accepted opinion, but in the context of this article refers to the process ''and'' the outcome of consensus decision-making (e.g. "to decide ''by'' consensus" and "''a'' consensus was reached"). History Consensus decision-making, as a self-described practice, originates from several nonviolent, direct action groups that were active in the Civil rights, Peace ...
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Consensus-based Assessment
Consensus-based assessment expands on the common practice of consensus decision-making and the theoretical observation that expertise can be closely approximated by large numbers of novices or journeymen. It creates a method for determining measurement standards for very ambiguous domains of knowledge, such as emotional intelligence, politics, religion, values and culture in general. From this perspective, the shared knowledge that forms cultural consensus can be assessed in much the same way as expertise or general intelligence. Measurement standards for general intelligence Consensus-based assessment is based on a simple finding: that samples of individuals with differing competence (e.g., experts and apprentices) rate relevant scenarios, using Likert scales, with similar mean ratings. Thus, from the perspective of a CBA framework, cultural standards for scoring keys can be derived from the population that is being assessed. Peter Legree and Joseph Psotka, working together o ...
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Global Energy Forecasting Competition
The Global Energy Forecasting Competition (GEFCom) is a competition conducted by a team led by Dr. Tao Hong that invites submissions around the world for forecasting energy demand. GEFCom was first held in 2012 on Kaggle, and the second GEFCom was held in 2014 on CrowdANALYTIX. GEFCom 2017 IEEE Working Group on Energy Forecasting opened Global Energy Forecasting Competition 2017 (GEFCom2017) termed: Hierarchical Probabilistic Load Forecasting. GEFCom2017 brought together state-of-the-art techniques and methodologies for hierarchical probabilistic energy forecasting. The competition featured a bi-level setup: a three-month qualifying match that included two tracks, and a one-month final match on a large-scale problem. In total 177 academic and company teams enrolled the competition. Qualifying match defined data track winners: * Ján Dolinský, Mária Starovská and Robert Toth (Tangent Works, Slovakia) * Andrew J. Landgraf (Battelle, USA) * Slawek Smyl (Uber Technologies, USA) a ...
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Quantile Regression
Quantile regression is a type of regression analysis used in statistics and econometrics. Whereas the method of least squares estimates the conditional ''mean'' of the response variable across values of the predictor variables, quantile regression estimates the conditional ''median'' (or other '' quantiles'') of the response variable. Quantile regression is an extension of linear regression used when the conditions of linear regression are not met. Advantages and applications One advantage of quantile regression relative to ordinary least squares regression is that the quantile regression estimates are more robust against outliers in the response measurements. However, the main attraction of quantile regression goes beyond this and is advantageous when conditional quantile functions are of interest. Different measures of central tendency and statistical dispersion can be useful to obtain a more comprehensive analysis of the relationship between variables. In ecology, quantile ...
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