DOCSLIB.ORG

The Regime Dependence of Optimally Weighted Ensemble Model Consensus Forecasts of Surface Temperature

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Regime Dependence of Optimally Weighted Ensemble Model Consensus Forecasts of Surface Temperature 1146 WEATHER AND FORECASTING VOLUME 23 The Regime Dependence of Optimally Weighted Ensemble Model Consensus Forecasts of Surface Temperature STEVEN J. GREYBUSH AND SUE ELLEN HAUPT Meteorology Department and Applied Research Laboratory, The Pennsylvania State University, University Park, Pennsylvania GEORGE S. YOUNG Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania (Manuscript received 3 October 2007, in final form 22 May 2008) ABSTRACT Previous methods for creating consensus forecasts weight individual ensemble members based upon their relative performance over the previous N days, implicitly making a short-term persistence assumption about the underlying flow regime. A postprocessing scheme in which model performance is linked to underlying weather regimes could improve the skill of deterministic ensemble model consensus forecasts. Here, prin- cipal component analysis of several synoptic- and mesoscale fields from the North American Regional Reanalysis dataset provides an objective means for characterizing atmospheric regimes. Clustering tech- niques, including K-means and a genetic algorithm, are developed that use the resulting principal compo- nents to distinguish among the weather regimes. This pilot study creates a weighted consensus from 48-h surface temperature predictions produced by the University of Washington Mesoscale Ensemble, a varied- model (differing physics and parameterization schemes) multianalysis ensemble with eight members. Dif- ferent optimal weights are generated for each weather regime. A second regime-dependent consensus technique uses linear regression to predict the relative performance of the ensemble members based upon the principal components. Consensus forecasts obtained by the regime-dependent schemes are compared using cross validation with traditional N-day ensemble consensus forecasts for four locations in the Pacific Northwest, and show improvement over methods that rely on the short-term persistence assumption. 1. Introduction mally combining the forecasts from a set of ensemble members to create a single deterministic consensus Ensemble modeling is proving to be a viable ap- forecast. These methods include using the simple en- proach for addressing uncertainty in numerical weather semble mean, linear regression in superensembles prediction (Hamill et al. 2000). Ensemble modeling cre- (Krishnamurti et al. 1999) and ensemble model output ates a distribution of potential forecast solutions by statistics (EMOS; Gneiting et al. 2005), performance- varying the initial conditions, boundary conditions, based metrics (Woodcock and Engel 2005), and Bayes- model physics, parameterization schemes, or the mod- ian model averaging (BMA; Raftery et al. 2005). In its els themselves. Ensemble models are run operationally simplest expression, the consensus forecast can be ex- at several meteorological centers, including the Na- pressed as a linear combination of individual ensemble tional Centers for Environmental Prediction (NCEP; member forecasts plus a bias correction term. The Toth and Kalnay 1997) and the European Centre for weight of a member in the combination typically is de- Medium-Range Weather Forecasts (ECWMF; Molteni termined by considering the performance of that mem- et al. 1996). ber relative to the other ensemble members over some Recent research has elucidated methods for opti- window of time. Most methods, such as that in Eckel and Mass (2005), employ a sliding window of dates, W, between 1 week and 2 months directly preceding the Corresponding author address: Sue Ellen Haupt, Applied Re- search Laboratory, The Pennsylvania State University, P.O. Box date the forecast is issued. Thus, the underlying as- 30, State College, PA 16804. sumption is that if member A performs relatively better E-mail: [email protected] than member B on the date the forecast verifies, then DOI: 10.1175/2008WAF2007078.1 © 2008 American Meteorological Society Unauthenticated | Downloaded 09/23/21 11:19 AM UTC WAF2007078 DECEMBER 2008 GREYBUSH ET AL. 1147 member A can be expected to continue to perform bet- TABLE 1. UWMEϩ dataset summary. ter than member B on the following day. This approach No. of members 8 members captures the effects of model evolution, but handles the Temporal domain 12 months (1 Sep 2005–31 Aug 2006) dependence of skill on atmospheric regime only to the Initialization time 0000 UTC (1600 PST) extent that the same regime persists throughout the Forecast time ϩ48 h period w. Domain size 12-km-spaced nested grid (Pacific Such atmospheric regimes have been recognized in Northwest) Forecast variable(s) 2-m temperature (Tsfc) models ranging from the very simple (Lorenz 2006) Surface verification Portland, OR; Astoria, OR; Olympia, WA; through full climate models (Branstator 1992). Fore- sites Redmond, OR casters have long understood that model performance is linked to the characteristics of the weather situation being forecast. For example, Fritsch et al. (2000) found postprocessing technique and, thus, test our underlying the magnitude of wintertime model temperature errors hypothesis that improved analysis of regime depen- to be related to the positioning of the jet stream. Fore- dence increases the success of ensemble member casters frequently subjectively weight the sources of in- weighting. formation used in developing their forecasts depending Section 2 describes the data used in this study and its upon such weather characteristics (Roebber 1998). Dif- preprocessing. The regime characterization techniques ferent model physics packages, such as cumulus param- are described in section 3, as are the types of regimes eterization schemes, reveal their relative strengths and found in the data. Section 4 explores methods of com- weaknesses under particular weather situations (Wang bining the data to obtain performance-weighted con- and Seaman 1997). As many ensembles employ a vari- sensus forecasts and section 5 elucidates our methods of ety of physics packages, such situational model skill obtaining regime-dependent consensus forecasts. Re- variations should also be present in ensemble model sults appear in section 6 and conclusions in section 7. output. Forecasters use these situational biases opera- tionally when, for example, they recognize that model 2. Ensemble model and verification data A is superior to model B when predicting heavy pre- cipitation events (Meng and Zhang 2007). One may The University of Washington Mesoscale Ensemble then surmise that an ensemble postprocessing system (UWMEϩ; Eckel and Mass 2005) is used as our source that considers these situational biases may improve of ensemble forecasts. The system is based upon the forecast skill. Clustering, the process through which fifth-generation Pennsylvania State University–Nation- similar elements of a dataset are grouped together, can al Center for Atmospheric Research Mesoscale Model be used to separate a time series of model forecasts into (MM5) and contains eight members. Each member has a finite set of weather situations or regimes. Here, a different initial and boundary conditions as well as dif- regime is defined broadly as any configuration of atmo- ferent physical/parameterization schemes. We use data spheric variable fields such that membership in a spe- from a nested 12-km-spaced grid roughly centered over cific regime can be determined objectively. Deloncle et the states of Washington and Oregon. Table 1 summa- al. (2007) have recently shown that artificial intelli- rizes the data downloaded from the UWME archives. gence methods, specifically k-nearest neighbor tech- To provide a relatively simple methodological test niques and random forests, can successfully identify bed, point data from four locations (Portland, Oregon; transitions between regimes. Astoria, Oregon; Olympia, Washington; and Redmond, This work explores the synthesis of consensus en- Oregon) are extracted from the UWMEϩ ensemble semble forecasting and weather regime identification. forecast dataset. Specifically, 2-m temperature data are We hypothesize that the relative performance of en- bilinearly interpolated at each location to create 1-yr semble members is linked to weather regimes. This hy- time series representing model forecasts from each en- pothesis of differing relative performances implies that semble member extending for the period from 1 Sep- varying the weights of ensemble members depending tember 2005 to 31 August 2006. The city of Portland is upon regimes should produce a more accurate consen- located along the banks of the Columbia River, with the sus forecast. Clustering forecast dates by weather re- Cascades to the east and Coast Range to the west. As- gime prior to computing skill and assigning perfor- toria Airport is located on the tidal flats of the Colum- mance-based weights to ensemble members may result bia, and is ringed by low mountains with the exception in reduced forecast error for a consensus deterministic of the west where lies the Pacific Ocean. Olympia is forecast. The historical methods explored here serve as located in the Puget Sound lowlands, and is separated benchmarks with which to judge the success of the new from the Pacific by the Coast Range. Redmond is lo- Unauthenticated | Downloaded 09/23/21 11:19 AM UTC 1148 WEATHER AND FORECASTING VOLUME 23 TABLE 2. Error statistics for all members of the UWMEϩ model, it represents a best guess at the true state of the ensemble evaluated at four locations. atmosphere at a given time. We choose to use the Location Bias
Load more

Recommended publications
  • WWP Wolfsburg Working Papers No. 12-01
    WWP Wolfsburg Working Papers No. 12-01 The improvement of annual economic forecasts by using non-annual indicators An empirical investigation for the G7 states Johannes Scheier, 2012 The improvement of annual economic forecasts by using non- annual indicators An Empirical Investigation for the G7 states Johannes Scheier Faculty of Business, Chair in Finance, Ostfalia University of Applied Sciences, D-38440 Wolfsburg, Germany Email: [email protected] Phone: +49 5361 8922 25450 Abstract A key demand made of business cycle forecasts is the efficient processing of freely available information. On the basis of two early indicators, this article shows that these opportunities are not being exploited. To this end, consensus forecasts for the economies of the G7 states from 1991-2009 were examined in this study. According to the present state of research, the forecasts are of moderate quality depending on the forecast horizon. The situation is different with regard to the early indicators which were examined. Both the results of a worldwide survey among experts by the Ifo Institute as well as the Composite Leading Indicators of the OECD exhibit a measurable correlation to the economic development considerably earlier for all countries. If viewed simultaneously, it can be seen that the forecast quality for most G7 states could be improved by taking the early indicators into consideration. Keywords: adjusting forecasts, business cycles, macroeconomic forecasts, evaluating forecasts, forecast accuracy, forecast efficiency, forecast monitoring, panel data, time series JEL classification: E17, E32, E37, E60 I. Introduction A wide range of demands are made of business cycle forecasts. The main demand is for them to be of good quality so that they can serve as a planning basis for states, companies and financial market participants.
    [Show full text]
  • Managing Functional Biases in Organizational Forecasts: a Case Study of Consensus Forecasting in Supply Chain Planning
    07-024 Managing Functional Biases in Organizational Forecasts: A Case Study of Consensus Forecasting in Supply Chain Planning Rogelio Oliva Noel Watson Copyright © 2007 by Rogelio Oliva and Noel Watson. Working papers are in draft form. This working paper is distributed for purposes of comment and discussion only. It may not be reproduced without permission of the copyright holder. Copies of working papers are available from the author. Managing Functional Biases in Organizational Forecasts: A Case Study of Consensus Forecasting in Supply Chain Planning Rogelio Oliva Mays Business School Texas A&M University College Station, TX 77843-4217 Ph 979-862-3744 | Fx 979-845-5653 [email protected] Noel Watson Harvard Business School Soldiers Field Rd. Boston, MA 02163 Ph 617-495-6614 | Fx 617-496-4059 [email protected] Draft: December 14, 2007. Do not quote or cite without permission from the authors. Managing Functional Biases in Organizational Forecasts: A Case Study of Consensus Forecasting in Supply Chain Planning Abstract To date, little research has been done on managing the organizational and political dimensions of generating and improving forecasts in corporate settings. We examine the implementation of a supply chain planning process at a consumer electronics company, concentrating on the forecasting approach around which the process revolves. Our analysis focuses on the forecasting process and how it mediates and accommodates the functional biases that can impair the forecast accuracy. We categorize the sources of functional bias into intentional, driven by misalignment of incentives and the disposition of power within the organization, and unintentional, resulting from informational and procedural blind spots.
    [Show full text]
  • Anchoring Bias in Consensus Forecasts and Its Effect on Market Prices
    Finance and Economics Discussion Series Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington, D.C. Anchoring Bias in Consensus Forecasts and its Effect on Market Prices Sean D. Campbell and Steven A. Sharpe 2007-12 NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminary materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the research staff or the Board of Governors. References in publications to the Finance and Economics Discussion Series (other than acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers. ANCHORING BIAS IN CONSENSUS FORECASTS AND ITS EFFECT ON MARKET PRICES* Sean D. Campbell Steven A. Sharpe February 2007 Previous empirical studies that test for the “rationality” of economic and financial forecasts generally test for generic properties such as bias or autocorrelated errors, and provide limited insight into the behavior behind inefficient forecasts. In this paper we test for a specific behavioral bias -- the anchoring bias described by Tversky and Kahneman (1974). In particular, we examine whether expert consensus forecasts of monthly economic releases from Money Market Services surveys from 1990-2006 have a tendency to be systematically biased toward the value of previous months’ data releases. We find broad- based and significant evidence for the anchoring hypothesis; consensus forecasts are biased towards the values of previous months’ data releases, which in some cases results in sizable predictable forecast errors. Then, to investigate whether the market participants anticipate the bias, we examine the response of interest rates to economic news.
    [Show full text]
  • Forecasting Spikes in Electricity Prices
    NCER Working Paper Series Forecasting Spikes in Electricity Prices T M Christensen A S Hurn K A Lindsay Working Paper #70 January 2011 Forecasting Spikes in Electricity Prices T M Christensen Department of Economics, Yale University A S Hurn School of Economics and Finance, Queensland University of Technology K A Lindsay Department of Mathematics, University of Glasgow. Abstract In many electricity markets, retailers purchase electricity at an unregulated spot price and sell to consumers at a heavily regulated price. Consequently the occurrence of extreme movements in the spot price represents a major source of risk to retailers and the accu- rate forecasting of these extreme events or price spikes is an important aspect of effective risk management. Traditional approaches to modeling electricity prices are aimed primarily at predicting the trajectory of spot prices. By contrast, this paper focuses exclusively on the prediction of spikes in electricity prices. The time series of price spikes is treated as a realization of a discrete-time point process and a nonlinear variant of the autoregressive conditional hazard (ACH) model is used to model this process. The model is estimated using half-hourly data from the Australian electricity market for the sample period 1 March 2001 to 30 June 2007. The estimated model is then used to provide one-step-ahead forecasts of the probability of an extreme event for every half hour for the forecast period, 1 July 2007 to 30 September 2007, chosen to correspond to the duration of a typical forward contract. The forecasting performance of the model is then evaluated against a benchmark that is consistent with the assumptions of commonly-used electricity pricing models.
    [Show full text]
  • Applying a Microfounded-Forecasting Approach to Predict Brazilian Inflation
    Applying a Microfounded-Forecasting Approach to Predict Brazilian Inflation Wagner Piazza Gaglianone, João Victor Issler and Silvia Maria Matos May, 2016 436 ISSN 1518-3548 CGC 00.038.166/0001-05 Working Paper Series Brasília n. 436 May 2016 p. 1-30 Working Paper Series Edited by Research Department (Depep) – E-mail: [email protected] Editor: Francisco Marcos Rodrigues Figueiredo – E-mail: [email protected] Co-editor: João Barata Ribeiro Blanco Barroso – E-mail: [email protected] Editorial Assistant: Jane Sofia Moita – E-mail: [email protected] Head of Research Department: Eduardo José Araújo Lima – E-mail: [email protected] The Banco Central do Brasil Working Papers are all evaluated in double blind referee process. Reproduction is permitted only if source is stated as follows: Working Paper n. 436. Authorized by Altamir Lopes, Deputy Governor for Economic Policy. General Control of Publications Banco Central do Brasil Comun/Dipiv/Coivi SBS – Quadra 3 – Bloco B – Edifício-Sede – 14º andar Caixa Postal 8.670 70074-900 Brasília – DF – Brazil Phones: +55 (61) 3414-3710 and 3414-3565 Fax: +55 (61) 3414-1898 E-mail: [email protected] The views expressed in this work are those of the authors and do not necessarily reflect those of the Banco Central or its members. Although these Working Papers often represent preliminary work, citation of source is required when used or reproduced. As opiniões expressas neste trabalho são exclusivamente do(s) autor(es) e não refletem, necessariamente, a visão do Banco Central do Brasil. Ainda que este artigo represente trabalho preliminar, é requerida a citação da fonte, mesmo quando reproduzido parcialmente.
    [Show full text]
  • Forecasting Macroeconomic Risks
    Federal Reserve Bank of New York Staff Reports Forecasting Macroeconomic Risks Patrick A. Adams Tobias Adrian Nina Boyarchenko Domenico Giannone Staff Report No. 914 February 2020 This paper presents preliminary findings and is being distributed to economists and other interested readers solely to stimulate discussion and elicit comments. The views expressed in this paper are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors. Forecasting Macroeconomic Risks Patrick A. Adams, Tobias Adrian, Nina Boyarchenko, and Domenico Giannone Federal Reserve Bank of New York Staff Reports, no. 914 February 2020 JEL classification: C22, E17, E37 Abstract We construct risks around consensus forecasts of real GDP growth, unemployment, and inflation. We find that risks are time-varying, asymmetric, and partly predictable. Tight financial conditions forecast downside growth risk, upside unemployment risk, and increased uncertainty around the inflation forecast. Growth vulnerability arises as the conditional mean and conditional variance of GDP growth are negatively correlated: downside risks are driven by lower mean and higher variance when financial conditions tighten. Similarly, employment vulnerability arises as the conditional mean and conditional variance of unemployment are positively correlated, with tighter financial conditions corresponding to higher forecasted unemployment and higher variance around the consensus forecast. Key words: macroeconomic uncertainty, quantile regressions, financial conditions _________________ Boyarchenko: Federal Reserve Bank of New York and CEPR (email: [email protected]). Adams: MIT Sloan (email: [email protected]). Adrian: International Monetary Fund and CEPR (email: [email protected]).
    [Show full text]
  • A New Approach to Predicting Analyst Forecast Errors: Do Investors Overweight Analyst Forecasts?$
    Journal of Financial Economics ] (]]]]) ]]]–]]] Contents lists available at SciVerse ScienceDirect Journal of Financial Economics journal homepage: www.elsevier.com/locate/jfec A new approach to predicting analyst forecast errors: Do investors overweight analyst forecasts?$ Eric C. So Massachusetts Institute of Technology, Sloan School of Management, E62-677, 100 Main Street, Cambridge, MA 02142, United States article info abstract Article history: I provide evidence that investors overweight analyst forecasts by demonstrating that Received 15 October 2011 prices do not fully reflect predictable components of analyst errors, which conflicts with Received in revised form conclusions in prior research. I highlight estimation bias in traditional approaches and 29 May 2012 develop a new approach that reduces this bias. I estimate characteristic forecasts that Accepted 12 June 2012 map current firm characteristics into forecasts of future earnings. Contrasting character- istic and analyst forecasts predicts analyst forecast errors and revisions. I find abnormal JEL classification: returns to strategies that sort firms by predicted forecast errors, consistent with investors G10 overweighting analyst forecasts and predictable biases in analyst forecasts influencing G11 the information content of prices. G14 & 2013 Elsevier B.V. All rights reserved. G17 G24 M41 Keywords: Analysts Forecast errors Earnings Market efficiency 1. Introduction Estimating a firm’s future profitability is an essential part of valuation analysis. Analysts can facilitate the $ I
    [Show full text]
  • Open Dau Nicholas Usingconsensusmodelingtoformulateamoreaccurateeconomicforecast.Pdf
    THE PENNSYLVANIA STATE UNIVERSITY SCHREYER HONORS COLLEGE DEPARTMENT OF ECONOMICS USING CONSENSUS MODELING TO FORMULATE A MORE ACCURATE ECONOMIC FORECAST NICHOLAS M. DAU SPRING 2015 A thesis submitted in partial fulfillment of the requirements for baccalaureate degrees in Economics and Political Science with honors in Economics Reviewed and approved* by the following: Andres Aradillas-Lopez Associate Professor of Economics Thesis Supervisor Russell Chuderewicz Senior Lecturer of Economics Honors Adviser * Signatures are on file in the Schreyer Honors College. i ABSTRACT This paper looks to forecast GDP growth, CPI change and 10-Year Treasury note interest rates through a consensus forecasting model. The consensus model takes into account 50-60 forecasts available from the Blue Chip Economic Indicators survey. The model differs from mainstream forecasts in that it assigns probabilities for each economic outcome, and supplements this with a broad 99% confidence interval. Ultimately the model performs best for current year CPI change and GDP growth to a lesser degree. The model struggles to provide precise and accurate forecasts for the long-term GDP, CPI and interest rate forecasts. ii TABLE OF CONTENTS LIST OF FIGURES ..................................................................................................... iii LIST OF TABLES ....................................................................................................... iv ACKNOWLEDGEMENTS ........................................................................................
    [Show full text]
  • A Re-Examination of Analysts' Superiority Over Time-Series Forecasts
    A re-examination of analysts’ superiority over time-series forecasts Mark T. Bradshaw Boston College Michael S. Drake The Ohio State University James N. Myers University of Arkansas Linda A. Myers University of Arkansas Draft: December 2009 Abstract: In this paper, we re-examine the widely-held belief that analysts’ earnings per share (EPS) forecasts are superior to forecasts from a time-series model. Using a naive random walk time-series model for annual earnings, we investigate whether and when analysts’ annual forecasts are superior. We also examine whether analysts’ forecasts approximate market expectations better than expectations from a simple random walk model. Our results indicate that simple random walk EPS forecasts are more accurate than analysts’ forecasts over longer forecast horizons and for firms that are smaller, younger, or have limited analyst following. Analysts’ superiority is less prevalent when analysts forecast large changes in EPS. These findings suggest an incomplete and misleading generalization about the superiority of analysts’ forecasts over even simple time-series-based earnings forecasts. Our findings imply that in certain settings, researchers can reliably use time-series-based forecasts in studies requiring earnings expectations. We thank workshop participants at the University of Akron, University of Arkansas, Florida State University, and Ohio State University for helpful suggestions and comments. James Myers gratefully acknowledges financial support from the Ralph L. McQueen Chair and Linda Myers gratefully acknowledges financial support from the Garrison/Wilson Chair, both at the University of Arkansas. 1. Introduction Research on analysts’ forecasts originated from a need within capital markets research to find a reliable proxy for investor expectations of earnings per share (EPS).
    [Show full text]
  • Partial Information Framework: Basic Theory and Applications
    University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2016 Partial Information Framework: Basic Theory and Applications Ville Antton Satopää University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Applied Mathematics Commons, Mathematics Commons, and the Statistics and Probability Commons Recommended Citation Satopää, Ville Antton, "Partial Information Framework: Basic Theory and Applications" (2016). Publicly Accessible Penn Dissertations. 1991. https://repository.upenn.edu/edissertations/1991 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/1991 For more information, please contact [email protected]. Partial Information Framework: Basic Theory and Applications Abstract Many real-world decisions depend on accurate predictions of some future outcome. In such cases the decision-maker often seeks to consult multiple people or/and models for their forecasts. These forecasts are then aggregated into a consensus that is inputted in the final decision-making process. Principled aggregation requires an understanding of why the forecasts are different. Historically, such forecast heterogeneity has been explained by measurement error. This dissertation, however, first shows that measurement error is not appropriate for modeling forecast heterogeneity and then introduces information diversity as a more appropriate yet fundamentally different alternative. Under information diversity differences in the forecasts stem purely from differences in the information that is used in the forecasts. This is made mathematically precise in a new modeling framework called the partial information framework. At its most general level, the partial information framework is a very reasonable model of multiple forecasts and hence offers an ideal platform for theoretical analysis.
    [Show full text]
  • The Accuracy and Efficiency of the Consensus Forecasts: a Further Application and Extension of the Pooled Approach
    Dis cus si on Paper No. 07-058 The Accuracy and Efficiency of the Consensus Forecasts: A Further Application and Extension of the Pooled Approach Philipp Ager, Marcus Kappler, and Steffen Osterloh Dis cus si on Paper No. 07-058 The Accuracy and Efficiency of the Consensus Forecasts: A Further Application and Extension of the Pooled Approach Philipp Ager, Marcus Kappler, and Steffen Osterloh Download this ZEW Discussion Paper from our ftp server: ftp://ftp.zew.de/pub/zew-docs/dp/dp07058.pdf Die Dis cus si on Pape rs die nen einer mög lichst schnel len Ver brei tung von neue ren For schungs arbei ten des ZEW. Die Bei trä ge lie gen in allei ni ger Ver ant wor tung der Auto ren und stel len nicht not wen di ger wei se die Mei nung des ZEW dar. Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW. Non-technical summary The last decade has seen marked economic fluctuations in the major industrial countries, which regularly present business cycle forecasters with a challenge. In this paper we are interested in how professional forecasters managed to predict GDP and price developments during the last decade. To this end, we explore the accuracy and evolution of the Consensus Forecast for twelve industrial countries for the years 1996 to 2006.
    [Show full text]
  • The IMF and OECD Versus Consensus Forecasts by Roy
    The IMF and OECD versus Consensus Forecasts by Roy Batchelor City University Business School, London August 2000 Abstract: This paper compares the accuracy and information content of macroeconomic economic forecasts for G7 countries made in the 1990s by the OECD and IMF. The benchmarks for comparison are the average forecasts of private sector economists published by Consensus Economics. With few exceptions, the private sector forecasts are less biased and more accurate in terms of mean absolute error and root mean square error. Formal tests show that there is little information in the OECD and IMF forecasts that could be used to reduce significantly the error in the private sector forecasts. Keywords: Forecasting, Combining Forecasts JEL Class No. : E27, E37 Acknowledgments: I am very grateful to Patrick R. Dennis for help in assembling the data used in this study. City University Business School Frobisher Crescent, Barbican, London EC2Y 8HB, UK. Phone:(-44-) 0-207 477 8733 Fax: (-44-) 0-207 477 8881 [email protected] 1 1. Introduction This study assesses the accuracy and value-added of the economic forecasts for G7 countries produced by the International Monetary Fund (IMF) and the Organization for Economic Cooperation and Development (OECD). The forecasts of the two agencies for the years 1990- 1999 are compared with a tough but relevant benchmark, the averages of private sector forecasts published by Consensus Economics. The study updates the comparisons made for an earlier period by Batchelor (2000). The quality of economic forecasts has been the subject of a broad debate in the press, the business community and government throughout the past decade.
    [Show full text]