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House Price Forecasts, Forecaster Herding, and the Recent Crisis  [PDF]
Christian Pierdzioch,Jan Christoph Rülke,Georg Stadtmann
International Journal of Financial Studies , 2013, DOI: 10.3390/ijfs1010016
Abstract: We used the Wall Street Journal survey data for the period 2006–2012 to analyze whether forecasts of house prices and housing starts provide evidence of (anti-)herding of forecasters. Forecasts are consistent with herding (anti-herding) of forecasters if forecasts are biased towards (away from) the consensus forecast. We found that anti-herding is prevalent among forecasters of house prices. We also report that, following the recent crisis, the prevalence of forecaster anti-herding seems to have changed over time.
Precipitation and temperature ensemble forecasts from single-value forecasts
J. Schaake,J. Demargne,R. Hartman,M. Mullusky
Hydrology and Earth System Sciences Discussions , 2007,
Abstract: A procedure is presented to construct ensemble forecasts from single-value forecasts of precipitation and temperature. This involves dividing the spatial forecast domain and total forecast period into a number of parts that are treated as separate forecast events. The spatial domain is divided into hydrologic sub-basins. The total forecast period is divided into time periods, one for each model time step. For each event archived values of forecasts and corresponding observations are used to model the joint distribution of forecasts and observations. The conditional distribution of observations for a given single-value forecast is used to represent the corresponding probability distribution of events that may occur for that forecast. This conditional forecast distribution subsequently is used to create ensemble members that vary in space and time using the "Schaake Shuffle" (Clark et al, 2004). The resulting ensemble members have the same space-time patterns as historical observations so that space-time joint relationships between events that have a significant effect on hydrological response tend to be preserved. Forecast uncertainty is space and time-scale dependent. For a given lead time to the beginning of the valid period of an event, forecast uncertainty depends on the length of the forecast valid time period and the spatial area to which the forecast applies. Although the "Schaake Shuffle" procedure, when applied to construct ensemble members from a time-series of single value forecasts, may preserve some of this scale dependency, it may not be sufficient without additional constraint. To account more fully for the time-dependent structure of forecast uncertainty, events for additional "aggregate" forecast periods are defined as accumulations of different "base" forecast periods. The generated ensemble members can be ingested by an Ensemble Streamflow Prediction system to produce ensemble forecasts of streamflow and other hydrological variables that reflect the meteorological uncertainty. The methodology is illustrated by an application to generate temperature and precipitation ensemble forecasts for the American River in California. Parameter estimation and dependent validation results are presented based on operational single-value forecasts archives of short-range River Forecast Center (RFC) forecasts and medium-range ensemble mean forecasts from the National Weather Service (NWS) Global Forecast System (GFS).
Exact phase transition of backtrack-free search with implications on the power of greedy algorithms  [PDF]
Liang Li,Tian Liu,Ke Xu
Computer Science , 2008,
Abstract: Backtracking is a basic strategy to solve constraint satisfaction problems (CSPs). A satisfiable CSP instance is backtrack-free if a solution can be found without encountering any dead-end during a backtracking search, implying that the instance is easy to solve. We prove an exact phase transition of backtrack-free search in some random CSPs, namely in Model RB and in Model RD. This is the first time an exact phase transition of backtrack-free search can be identified on some random CSPs. Our technical results also have interesting implications on the power of greedy algorithms, on the width of random hypergraphs and on the exact satisfiability threshold of random CSPs.
Dominating countably many forecasts  [PDF]
M. J. Schervish,Teddy Seidenfeld,J. B. Kadane
Statistics , 2014, DOI: 10.1214/14-AOS1203
Abstract: We investigate differences between a simple Dominance Principle applied to sums of fair prices for variables and dominance applied to sums of forecasts for variables scored by proper scoring rules. In particular, we consider differences when fair prices and forecasts correspond to finitely additive expectations and dominance is applied with infinitely many prices and/or forecasts.
THE ACCURACY OF UNEMPLOYMENT RATE FORECASTS IN ROMANIA AND THE ACTUAL ECONOMIC CRISIS  [PDF]
Mihaela BRATU (SIMIONESCU)
Scientific Bulletin : Economic Sciences , 2012,
Abstract: In this study, the problem of forecasts accuracy is analysed on three different forecasting horizons: during the actual economic crisis, in few years before the crisis and on a large horizon. The accuracy of the forecasts made by European Commission, National Commission for Prognosis (NCP) and Institute for Economic Forecasting (IEF) for unemployment rate in Romania is assessed. The most accurate predictions on the forecasting horizons 2001-2011 and 2009-2011 were provided by IEF and the less accurate by NCP. These results were gotten using U1 Theil’s statistic and a new method that has not been used before in literature in this context. The multi-criteria ranking was applied to make a hierarchy of the institutions regarding the accuracy and five important accuracy measures were taken into account at the same time: mean errors, mean squared error, root mean squared error, U1 and U2 statistics of Theil. In few years before crisis (2006-2008) another hierarchy of institutions were gotten using the accuracy criterion: NCP, IEF and EC. The combined forecasts of institutions’ predictions are the best strategy to improve the forecasts accuracy on overall and before the crisis. During the economic crisis IEF provided the most accurate predictions, the combined forecasts being a good strategy of improving only the forecasts made by NCP and EC using inversely MSE scheme and equally weighted scheme. The assessment and improvement of forecasts accuracy have an important contribution in growing the quality of decisional process.
On the reliability of Seasonal Climate Forecasts  [PDF]
Antje Weisheimer,T. N. Palmer
Physics , 2013,
Abstract: Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: How good are seasonal climate forecasts on a scale of 1-5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal climate forecasts are made from ensembles of integrations of numerical models of climate. We argue that goodness should be assessed primarily in terms of the probabilistic reliability of these ensemble-based forecasts and that a 5 should be reserved for systems which are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as a world leading operational institute producing seasonal climate forecasts. A wide range of goodness rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching 5 across all regions and variables in 30 years time.
Ocean Initialization for Seasonal Forecasts
Magdalena A. Balmaseda,Oscar Alves,Alberto Arribas,Toshiyuki Awaji
Oceanography , 2009,
Abstract: Several operational centers routinely issue seasonal forecasts of Earth’s climate using coupled ocean-atmosphere models, which require near-real-time knowledge of the state of the global ocean. This paper reviews existing ocean analysis efforts aimed at initializing seasonal forecasts. We show that ocean data assimilation improves the skill of seasonal forecasts in many cases, although its impact can be overshadowed by errors in the coupled models. The current practice, known as “uncoupled” initialization, has the advantage of better knowledge of atmospheric forcing fluxes, but it has the shortcoming of potential initialization shock. In recent years, the idea of obtaining truly “coupled” initialization, where the different components of the coupled system are well balanced, has stimulated several research activities that will be reviewed in light of their application to seasonal forecasts.
Use and Communication of Probabilistic Forecasts  [PDF]
Adrian E. Raftery
Statistics , 2014,
Abstract: Probabilistic forecasts are becoming more and more available. How should they be used and communicated? What are the obstacles to their use in practice? I review experience with five problems where probabilistic forecasting played an important role. This leads me to identify five types of potential users: Low Stakes Users, who don't need probabilistic forecasts; General Assessors, who need an overall idea of the uncertainty in the forecast; Change Assessors, who need to know if a change is out of line with expectatations; Risk Avoiders, who wish to limit the risk of an adverse outcome; and Decision Theorists, who quantify their loss function and perform the decision-theoretic calculations. This suggests that it is important to interact with users and to consider their goals. The cognitive research tells us that calibration is important for trust in probability forecasts, and that it is important to match the verbal expression with the task. The cognitive load should be minimized, reducing the probabilistic forecast to a single percentile if appropriate. Probabilities of adverse events and percentiles of the predictive distribution of quantities of interest seem often to be the best way to summarize probabilistic forecasts. Formal decision theory has an important role, but in a limited range of applications.
Evaluating probability forecasts  [PDF]
Tze Leung Lai,Shulamith T. Gross,David Bo Shen
Statistics , 2012, DOI: 10.1214/11-AOS902
Abstract: Probability forecasts of events are routinely used in climate predictions, in forecasting default probabilities on bank loans or in estimating the probability of a patient's positive response to treatment. Scoring rules have long been used to assess the efficacy of the forecast probabilities after observing the occurrence, or nonoccurrence, of the predicted events. We develop herein a statistical theory for scoring rules and propose an alternative approach to the evaluation of probability forecasts. This approach uses loss functions relating the predicted to the actual probabilities of the events and applies martingale theory to exploit the temporal structure between the forecast and the subsequent occurrence or nonoccurrence of the event.
Predicting Failures of Point Forecasts  [PDF]
S. Hallerberg,J. Br?cker,H. Kantz,L. A. Smith
Physics , 2011,
Abstract: The predictability of errors in deterministic temperature forecasts is investigated. More precisely, the aim is to issue warnings whenever the differences between forecast and verification exceed a given threshold. The warnings are generated by analyzing the output of an ensemble forecast system in terms of a decision making approach. The quality of the resulting predictions is evaluated by computing receiver operating characteristics, the Brier score, and the Ignorance score. Special emphasis is also given to the question whether rare events are better predictable.
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