Adjusting Judgemental Extrapolations using Theil's Method and Discounted Weighted Regression

Goodwin, Paul

doi:10.1002/(sici)1099-131x(199701)16:1<37::aid-for647>3.0.co;2-t

Cited by 29 publications

(13 citation statements)

References 18 publications

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“…In the context of judgmental point forecasting, Goodwin (1997) describes an approach that allows the debiasing regression parameters to vary over time if there is a changing relationship between actuals and point forecasts, such as when the quality of the forecasts are considered to have improved over time. In view of the developments in the ensemble generating system, such as the introduction of stochastic physics in October 1998, there is some appeal to debiasing the ensemble member quantiles using time varying parameters (TVP).…”

Section: Methods Q5 -Ensemble Quantiles Debiased Using Tvp Quantile Rementioning

confidence: 99%

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A comparison of temperature density forecasts from GARCH and atmospheric models

Taylor

Buizza

2004

Journal of Forecasting

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Density forecasts for weather variables are useful for the many industries exposed to weather risk. Weather ensemble predictions are generated from atmospheric models and consist of multiple future scenarios for a weather variable. The distribution of the scenarios can be used as a density forecast, which is needed for pricing weather derivatives. We consider one to 10 dayahead density forecasts provided by temperature ensemble predictions. More specifically, we evaluate forecasts of the mean and quantiles of the density. The mean of the ensemble scenarios is the most accurate forecast for the mean of the density. We use quantile regression to debias the quantiles of the distribution of the ensemble scenarios. The resultant quantile forecasts compare favourably with those from a GARCH model. These results indicate the strong potential for the use of ensemble prediction in temperature density forecasting.

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Section: Methods Q5 -Ensemble Quantiles Debiased Using Tvp Quantile Rementioning

confidence: 99%

“…The use of OLS regression to debias a point forecast was proposed by Theil (1971). In the context of judgmental point forecasting, Goodwin (1997) describes an approach that allows the debiasinĝ…”

Section: Methods Q5: Ensemble Quantiles Debiased Using Tvp Quantile Rementioning

confidence: 99%

A comparison of temperature density forecasts from GARCH and atmospheric models

Taylor

Buizza

2004

Journal of Forecasting

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“…The expression in (6) is commonly used outside of psychology (Murphy, 1988;Solazzo, Hogrefe, Colette, Garcia-Vivanco, & Galmarini, 2017). Building upon this formulation, the lower bound for prediction error can be seen in the Theil decomposition for the sample-based MSE (Bento, Salvação, & Guedes Soares, 2018;Fitzgerald & Akintoye, 1995;Goodwin, 1997;Theil, 1961). This can be written as…”

Section: Theil Mse Decompositionmentioning

confidence: 99%

Why Overfitting is Not (Usually) a Problem in Partial Correlation Networks

Dr¹,

Rodriguez²

2020

Preprint

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Network psychometrics is undergoing a time of methodological reflection. In part, this was spurred by the revelation that l1-regularization does not reduce spurious associations in partial correlation networks. In this work, we address another motivation for the widespread use of regularized estimation: the thought that it is needed to mitigate overfitting. We first clarify important aspects of overfitting and the bias-variance tradeoff that are especially relevant for the network literature, where the number of nodes or items in a psychometric scale are not largecompared to the number of observations (i.e., a low p/n ratio). This revealed that bias and especially variance are most problematic in p=n ratios rarely encountered. We then introduce a nonregularized method, based on classical hypothesis testing, that fulfills two desiderata: (1) reducing or controlling the false positives rate and (2) quelling concerns of overfitting by providing accurate predictions. These were the primary motivations for initially adopting the graphical lasso (glasso). In several simulation studies, our nonregularized method provided more than competitive predictive performance, and, in many cases, outperformed glasso. Itappears to be nonregularized, as opposed to regularized estimation, that best satisfies these desiderata. We then provide insights into using our methodology. Here we discuss the multiple comparisons problem in relation to prediction: stringent alpha levels, resulting in a sparse network, can deteriorate predictive accuracy. We end by emphasizing key advantages of our approach that make it ideal for both inference and prediction in network analysis.

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“…The discounted weighted regression (DWR) model proposed by Ameen and Harrison (1984) utilizes a recursive process to generate parameters for Theil's (1971) model. Goodwin (1997) found that the DWR model outperformed Theil's model when sudden changes in demand occurred. Jex (1994) also used a weighed correction model to improve forecast accuracy when systematic bias is a problem.…”

Section: Bias In Hierarchical Forecastingmentioning

confidence: 99%

Reducing Bias in Hierarchical Forecasting

Utley¹

2014

Advances in Business and Management Forecasting

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This paper presents a mathematical programming model to reduce bias for both aggregate demand forecasts and lower echelon forecasts comprising a hierarchical forecasting system. Demand data from an actual service operation are used to illustrate the model and compare its accuracy with a standard approach for hierarchical forecasting. Results show that the proposed methodology outperforms the standard approach.

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Adjusting Judgemental Extrapolations using Theil's Method and Discounted Weighted Regression

Cited by 29 publications

References 18 publications

A comparison of temperature density forecasts from GARCH and atmospheric models

A comparison of temperature density forecasts from GARCH and atmospheric models

Why Overfitting is Not (Usually) a Problem in Partial Correlation Networks

Reducing Bias in Hierarchical Forecasting

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