Combining Ranked Mean Value Forecasts PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Combining Ranked Mean Value Forecasts PDF full book. Access full book title Combining Ranked Mean Value Forecasts by Mehdi Mostaghimi. Download full books in PDF and EPUB format.
Author: Mehdi Mostaghimi
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
In modeling a combination of forecasts all the information related to the past performance of the individual forecasts, including accuracy and correlation, is considered. In this paper I have extended the modeling to incorporate a rank ordering of the forecasts by a decision maker. This ordering could be based on the expectations of a decision maker or on the judgment of an expert about the relative future performance of the forecasts. The problem is set up as a likelihood function of the individual forecasts given the combined forecast. It is shown that this likelihood function is approximately an exponential function of a relative entropy information measure. The maximum likelihood combined forecast is a weighted linear function of the individual forecasts, where the weights are a function of the past performance of the individual forecasts, the correlations between the forecasts and the decision maker's ranking of the forecasts. It is shown that ranking is effective only when the forecasts are correlated: the greater the correlation, the more effective the ranking. A sample application of this methodology to forecasting U.S. hog prices shows that ordering forecasts according to their individual performances produces a very robust and accurate combined forecast; however, this forecast is not the most accurate among the combined forecasts.
Author: Mehdi Mostaghimi
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
In modeling a combination of forecasts all the information related to the past performance of the individual forecasts, including accuracy and correlation, is considered. In this paper I have extended the modeling to incorporate a rank ordering of the forecasts by a decision maker. This ordering could be based on the expectations of a decision maker or on the judgment of an expert about the relative future performance of the forecasts. The problem is set up as a likelihood function of the individual forecasts given the combined forecast. It is shown that this likelihood function is approximately an exponential function of a relative entropy information measure. The maximum likelihood combined forecast is a weighted linear function of the individual forecasts, where the weights are a function of the past performance of the individual forecasts, the correlations between the forecasts and the decision maker's ranking of the forecasts. It is shown that ranking is effective only when the forecasts are correlated: the greater the correlation, the more effective the ranking. A sample application of this methodology to forecasting U.S. hog prices shows that ordering forecasts according to their individual performances produces a very robust and accurate combined forecast; however, this forecast is not the most accurate among the combined forecasts.
Author:
Publisher:
ISBN:
Category : Statistics
Languages : en
Pages : 722
Get Book Here
Book Description
Author: Antonio Bracale
Publisher: MDPI
ISBN: 303928312X
Category : Technology & Engineering
Languages : en
Pages : 134
Get Book Here
Book Description
Modern power systems are affected by many sources of uncertainty, driven by the spread of renewable generation, by the development of liberalized energy market systems and by the intrinsic random behavior of the final energy customers. Forecasting is, therefore, a crucial task in planning and managing modern power systems at any level: from transmission to distribution networks, and in also the new context of smart grids. Recent trends suggest the suitability of ensemble approaches in order to increase the versatility and robustness of forecasting systems. Stacking, boosting, and bagging techniques have recently started to attract the interest of power system practitioners. This book addresses the development of new, advanced, ensemble forecasting methods applied to power systems, collecting recent contributions to the development of accurate forecasts of energy-related variables by some of the most qualified experts in energy forecasting. Typical areas of research (renewable energy forecasting, load forecasting, energy price forecasting) are investigated, with relevant applications to the use of forecasts in energy management systems.
Author: Jeremy Kwok
Publisher: Vernon Press
ISBN: 164889268X
Category : Business & Economics
Languages : en
Pages : 242
Get Book Here
Book Description
‘Macroeconometric Models for Portfolio Management’ begins by outlining a portfolio management framework into which macroeconometric models and backtesting investment strategies are integrated. It is followed by a discussion on the theoretical backgrounds of both small and global large macroeconometric models, including data selection, estimation, and applications. Other practical concerns essential to managing a portfolio with decisions driven by macro models are also covered: model validation, forecast combination, and evaluation. The author then focuses on applying these models and their results on managing the portfolio, including making trading rules and asset allocation across different assets and risk management. The book finishes by showing portfolio examples where different investment strategies are used and illustrate how the framework can be applied from the beginning of collecting data, model estimation, and generating forecasts to how to manage portfolios accordingly. This book aims to bridge the gap between academia and practising professionals. Readers will attain a rigorous understanding of the theory and how to apply these models to their portfolios. Therefore, ‘Macroeconometric Models for Portfolio Management’ will be of interest to academics and scholars working in macroeconomics and finance; to industry professionals working in financial economics and asset management; to asset managers and investors who prefer systematic investing over discretionary investing; and to investors who have a strong interest in macroeconomic influences on their portfolio.
Author: International Monetary
Publisher: International Monetary Fund
ISBN:
Category : Business & Economics
Languages : en
Pages : 38
Get Book Here
Book Description
The currency in circulation forecasting model presently used by the Central Bank of Jordan is aligned with international practices and provides a solid basis for liquidity management. The central bank uses an Auto Regressive Integrated Moving Average (ARIMA) model with many indicator variables to model binary seasonality and to capture special events. The ARIMA model is fitted on daily currency in circulation data using a standard maximum likelihood estimator. This ARIMA approach is aligned with the models traditionally used by central banks in emerging and middle-income countries.
Author: Ly, Racine
Publisher: Intl Food Policy Res Inst
ISBN:
Category : Political Science
Languages : en
Pages : 26
Get Book Here
Book Description
This paper applies a recurrent neural network (RNN) method to forecast cotton and oil prices. We show how these new tools from machine learning, particularly Long-Short Term Memory (LSTM) models, complement traditional methods. Our results show that machine learning methods fit reasonably well with the data but do not outperform systematically classical methods such as Autoregressive Integrated Moving Average (ARIMA) or the naïve models in terms of out of sample forecasts. However, averaging the forecasts from the two type of models provide better results compared to either method. Compared to the ARIMA and the LSTM, the Root Mean Squared Error (RMSE) of the average forecast was 0.21 and 21.49 percent lower, respectively, for cotton. For oil, the forecast averaging does not provide improvements in terms of RMSE. We suggest using a forecast averaging method and extending our analysis to a wide range of commodity prices.
Author: G. Elliott
Publisher: Elsevier
ISBN: 0080460674
Category : Business & Economics
Languages : en
Pages : 1071
Get Book Here
Book Description
Research on forecasting methods has made important progress over recent years and these developments are brought together in the Handbook of Economic Forecasting. The handbook covers developments in how forecasts are constructed based on multivariate time-series models, dynamic factor models, nonlinear models and combination methods. The handbook also includes chapters on forecast evaluation, including evaluation of point forecasts and probability forecasts and contains chapters on survey forecasts and volatility forecasts. Areas of applications of forecasts covered in the handbook include economics, finance and marketing.*Addresses economic forecasting methodology, forecasting models, forecasting with different data structures, and the applications of forecasting methods *Insights within this volume can be applied to economics, finance and marketing disciplines
Author: Peter Fuleky
Publisher: Springer Nature
ISBN: 3030311503
Category : Business & Economics
Languages : en
Pages : 716
Get Book Here
Book Description
This book surveys big data tools used in macroeconomic forecasting and addresses related econometric issues, including how to capture dynamic relationships among variables; how to select parsimonious models; how to deal with model uncertainty, instability, non-stationarity, and mixed frequency data; and how to evaluate forecasts, among others. Each chapter is self-contained with references, and provides solid background information, while also reviewing the latest advances in the field. Accordingly, the book offers a valuable resource for researchers, professional forecasters, and students of quantitative economics.
Author: J.S. Armstrong
Publisher: Springer Science & Business Media
ISBN: 0306476304
Category : Business & Economics
Languages : en
Pages : 840
Get Book Here
Book Description
Principles of Forecasting: A Handbook for Researchers and Practitioners summarizes knowledge from experts and from empirical studies. It provides guidelines that can be applied in fields such as economics, sociology, and psychology. It applies to problems such as those in finance (How much is this company worth?), marketing (Will a new product be successful?), personnel (How can we identify the best job candidates?), and production (What level of inventories should be kept?). The book is edited by Professor J. Scott Armstrong of the Wharton School, University of Pennsylvania. Contributions were written by 40 leading experts in forecasting, and the 30 chapters cover all types of forecasting methods. There are judgmental methods such as Delphi, role-playing, and intentions studies. Quantitative methods include econometric methods, expert systems, and extrapolation. Some methods, such as conjoint analysis, analogies, and rule-based forecasting, integrate quantitative and judgmental procedures. In each area, the authors identify what is known in the form of `if-then principles', and they summarize evidence on these principles. The project, developed over a four-year period, represents the first book to summarize all that is known about forecasting and to present it so that it can be used by researchers and practitioners. To ensure that the principles are correct, the authors reviewed one another's papers. In addition, external reviews were provided by more than 120 experts, some of whom reviewed many of the papers. The book includes the first comprehensive forecasting dictionary.
Author: Stéphane Vannitsem
Publisher: Elsevier
ISBN: 012812248X
Category : Science
Languages : en
Pages : 364
Get Book Here
Book Description
Statistical Postprocessing of Ensemble Forecasts brings together chapters contributed by international subject-matter experts describing the current state of the art in the statistical postprocessing of ensemble forecasts. The book illustrates the use of these methods in several important applications including weather, hydrological and climate forecasts, and renewable energy forecasting. After an introductory section on ensemble forecasts and prediction systems, the second section of the book is devoted to exposition of the methods available for statistical postprocessing of ensemble forecasts: univariate and multivariate ensemble postprocessing are first reviewed by Wilks (Chapters 3), then Schefzik and Möller (Chapter 4), and the more specialized perspective necessary for postprocessing forecasts for extremes is presented by Friederichs, Wahl, and Buschow (Chapter 5). The second section concludes with a discussion of forecast verification methods devised specifically for evaluation of ensemble forecasts (Chapter 6 by Thorarinsdottir and Schuhen). The third section of this book is devoted to applications of ensemble postprocessing. Practical aspects of ensemble postprocessing are first detailed in Chapter 7 (Hamill), including an extended and illustrative case study. Chapters 8 (Hemri), 9 (Pinson and Messner), and 10 (Van Schaeybroeck and Vannitsem) discuss ensemble postprocessing specifically for hydrological applications, postprocessing in support of renewable energy applications, and postprocessing of long-range forecasts from months to decades. Finally, Chapter 11 (Messner) provides a guide to the ensemble-postprocessing software available in the R programming language, which should greatly help readers implement many of the ideas presented in this book. Edited by three experts with strong and complementary expertise in statistical postprocessing of ensemble forecasts, this book assesses the new and rapidly developing field of ensemble forecast postprocessing as an extension of the use of statistical corrections to traditional deterministic forecasts. Statistical Postprocessing of Ensemble Forecasts is an essential resource for researchers, operational practitioners, and students in weather, seasonal, and climate forecasting, as well as users of such forecasts in fields involving renewable energy, conventional energy, hydrology, environmental engineering, and agriculture. - Consolidates, for the first time, the methodologies and applications of ensemble forecasts in one succinct place - Provides real-world examples of methods used to formulate forecasts - Presents the tools needed to make the best use of multiple model forecasts in a timely and efficient manner
