Benchmark Forecast and Error Modeling. (18th September 2016)

• Record Type: Journal Article
• Authors: Chen, Zhao‐Guo; Wu, Ka Ho
• Is Part Of: Journal of forecasting. Volume 36:Number 4(2017)
• Page Start: 382
• Page End: 394
• Publication Date: 2016-09-18
• Subjects: ARMA model -- autocorrelation -- benchmarking prediction -- regression -- standard deviation; Forecasting -- Periodicals; Forecasting -- Mathematical models -- Periodicals; 003.2
• DOI: 10.1002/for.2439
• Languages: English
• ISSNs: 0277-6693

Abstract

Abstract : For a target socioeconomic variable with data from two sources, benchmarking is a process which uses less frequent and more reliable data, called benchmarks, to adjust more frequent and less reliable data. Consequently, forecasts of unknown benchmarks are obtained. The regression method of benchmarking may lead to better results than widely used numerical methods, but the model for the error of the more frequent data is supposed to be known. By properly choosing a first‐order autoregressive model as 'working model' for the error, the regression method may work well in reality. We present two new error modeling procedures via inside‐data‐period benchmark forecasts. The performance of several modeling procedures is compared. These results may provide analysts with guidelines for choosing working models for the error in developing and applying benchmarking software. Copyright © 2016 John Wiley & Sons, Ltd.