459:, but these can be minimised by combining independent forecasts together. Hence, combining is seen as helping to improve forecast accuracy by reducing the forecast errors of individual forecasts. Furthermore, averaging forecasts is likely to be more useful when the data and the forecasting techniques that the component forecasts are drawn from differ substantially. And even though it is only a simple approach (typically an unweighted mean average), this method is just as useful as other more sophisticated models. Indeed, more recent studies in the past decade have shown that, over time, the equal weights combined forecast is usually more accurate than the individual forecast which make up the consensus.
475:. A study by Robert C. Jones found: "At least since the publication of βThe Combination of Forecastsβ (Bates and Granger ), economists have known that combining forecasts from different sources can both improve accuracy and reduce forecaster error. In the intervening years, numerous studies have confirmed these conclusions, outlined conditions under which forecast combinations are most effective, and tried to explain why simple equal weights work so well relative to more sophisticated statistical techniques.β
38:
455:
method is identified as the best, combining is still worthwhile if other methods can make some positive contribution to the forecast accuracy. Moreover, many factors can affect the independent forecast and these, along with any additional useful information, might be captured by using the consensus approach. Another argument in favour of this method is that individual forecasts may be subject to numerous
425:, the future path of the economy is intrinsic to almost every company's business outlook, and hence there is considerable demand for accurate economic forecasts. Matching this strong demand is the large volume of readily available forecast information from governments, international agencies and various private firms. Companies such as
433:
specialise in publishing economic forecast data, with the former covering most major regions of the world as well as providing forecasts for currencies and commodities, and the latter focusing on the US economy. However, deciphering the best forecast method is no easy task, and largely depends on the
449:
compared to other individual forecasts can be identified, adopting the consensus approach can be beneficial due to diversification gains. Combining economic forecasts is well established in many countries and can count central banks, government institutions and businesses among the users. In recent
454:
show that pooling forecasts increased forecast accuracy. One of the advantages of using consensus forecasts is that it can prove useful if there is a high degree of uncertainty or risk attached to the situation and the selection of the most accurate forecast in advance is difficult. Even if one
462:
In sum, the usefulness of the consensus forecast technique has been supported by a wealth of empirical studies in recent decades. The use of equal weights in the combining method is appealing because of its simplicity and is easy to describe. Among others, this simple method of averaging the
420:
plays a key role in any organisation's planning process as it provides insight into uncertainty. Through simulation, one will be able to assess whether proposed strategies are likely to produce the desired objectives within predefined limits. In the field of
434:
objectives of the user and the constraints they are likely to face. Rather than try to identify a single best forecasting method, an alternative approach is to combine the results from independent forecasters and take an average of the forecasts.
604:
463:
forecasts of individual forecasters has been put into practice by many of the world's central banks as they try to gauge expectations in the private sector. An empirical study carried out by
350:
is a prediction of the future created by combining several separate forecasts which have often been created using different methodologies. They are used in a number of sciences, ranging from
491:, however, there has been some progress in the area of density forecasts. A simple, yet powerful alternative technique has been introduced in the context of electricity price forecasting.
326:
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to the point forecasts of a number of individual forecasting models or experts. It has been found to perform extremely well in practice - the top two performing teams in the
472:
619:
445:, is known as combining forecasts and the result is often referred to as a consensus forecast. Unless a particular forecast model which produces smaller
405:
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
467:
in 2000 demonstrates greater accuracy in the consensus forecasts over macroeconomic projections produced by leading multinational agencies such as the
450:
decades, consensus forecasts have attracted much interest, backed by the publication of a huge swathe of academic research on forecast accuracy.
940:
Mitchell, James; Wallis, Kenneth F. (2011). "Evaluating density forecasts: forecast combinations, model mixtures, calibration and sharpness".
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Blix, Marten; Wadefjord, Joachim; Wienecke, Ulrika; Adahl, Martin (2001). "How good is the forecasting performance of major institutions?".
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Golinelli, R.; Parigi, G. (2008). "Real-Time
Squared: A Real-Time Data Set for Real-Time GDP Forecasting".
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409:, they can also be for one-off events such as the outcome of a presidential election or a football match.
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987:"Computing electricity spot price prediction intervals using quantile regression and forecast averaging"
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19:"Consensus forecasts" redirects here. For Consensus Forecasts published by Consensus Economics, see
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Although the literature on the combination of point forecasts is very rich, the topic of combining
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Wallis, Kenneth F. (2005). "Combining
Density and Interval Forecasts: A Modest Proposal*".
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is not so popular. There are very few papers that deal explicitly with the combination of
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Makridakis; Hibon (2000). "The M3-Competition: Results, Conclusions and
Implications".
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Clemen, Robert T. (1989). "Combining forecasts: A review and annotated bibliography".
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McNees, Stephen K. (November 1987). "Consensus
Forecasts: Tyranny of the Majority".
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Timmermann, Allan (2007). "An
Evaluation of the World Economic Outlook Forecasts".
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605:"Assessment of Consensus Forecasts Accuracy: The Czech National Bank Perspective"
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Timmermann, Allan (2006). G. Elliott, C. W. J. Granger and A. Timmermann (ed.).
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Applications can range from forecasting the weather to predicting the annual
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Prediction of the future created by combining several separate forecasts
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Batchelor, Roy (2000). "The IMF and OECD versus
Consensus Forecasts".
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Jones, Robert C. (2014). "Making Better
Investment Decisions".
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of a panel of independent forecasts, derived from different
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Finance a Uver: Czech
Journal of Economics & Finance
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Organisation for
Economic Co-operation and Development
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603:Novotny, Filip; Rakova, Marie (2011).
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505:Global Energy Forecasting Competition
800:International Journal of Forecasting
750:International Journal of Forecasting
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652:International Journal of Forecasting
854:The Journal of Portfolio Management
547:Quantile Regression Averaging (QRA)
507:(GEFCom2014) used variants of QRA.
493:Quantile Regression Averaging (QRA)
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699:. Vol. 1. pp. 135β196.
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911:10.1111/j.1468-0084.2005.00148.x
812:10.1016/j.ijforecast.2008.05.001
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942:Journal of Applied Econometrics
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618:(4): 348β366. Archived from
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552:Reference class forecasting
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469:International Monetary Fund
293:Science fiction prototyping
225:Reference class forecasting
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517:Consensus-based assessment
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1057:Macroeconomic forecasting
1004:10.1007/s00180-014-0523-0
866:10.3905/jpm.2014.40.2.128
522:Consensus decision-making
991:Computational Statistics
358:, and are also known as
65:Global catastrophic risk
1052:Statistical forecasting
485:probabilistic forecasts
479:Probabilistic forecasts
278:Exploratory engineering
168:Causal layered analysis
839:10.1080/00036840121785
403:Gross Domestic Product
105:Historical materialism
183:Cross impact analysis
1037:Economic forecasting
542:Ensemble forecasting
532:Economic forecasting
423:economic forecasting
1042:Informal estimation
497:quantile regression
443:forecasting methods
427:Consensus Economics
360:combining forecasts
308:Technology scouting
55:Accelerating change
21:Consensus Economics
1032:Futures techniques
537:Ensemble averaging
495:involves applying
489:interval forecasts
457:behavioural biases
392:expert aggregation
387:ensemble averaging
381:committee machines
364:forecast averaging
348:consensus forecast
298:Speculative design
178:Consensus forecast
173:Chain-linked model
135:Resource depletion
827:Applied Economics
452:Empirical studies
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93:Space exploration
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205:Future-proof
177:
895:: 983β994.
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501:price track
418:Forecasting
407:time series
356:meteorology
259:forecasting
253:Technology
163:Backcasting
140:Singularity
120:Moore's law
78:Mathematics
1026:Categories
638:References
629:2012-06-27
413:Background
376:statistics
288:Hype cycle
255:assessment
154:Techniques
1013:0943-4062
972:1099-1255
950:CiteSeerX
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