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Noisy Data and Uncertain Coefficients: A Comment

John H. Herbert

Year: 1989
Volume: Volume 10
Number: Number 1
DOI: 10.5547/ISSN0195-6574-EJ-Vol10-No1-15
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Abstract:
Noisy data are frequently used to estimate regression coefficients for energy demand equations. Although analysts new to the area might expect reported coefficients and forecasts to reflect this source of uncertainty, reported numbers, in general, do not reflect it. Recently, Kher et al. (1987, hereinafter KSS) confronted the problem and proposed a new technique for reporting forecasts based on the estimation of bounds for regression coefficients. Regression coefficient bounds reflect the uncertainty in an estimated model stemming from the use of noisy data. In this comment I identify additional studies that have addressed the estimation of such bounds and also present an example of a more common use of them.



Noisy Data and Uncertain Coefficients: A Reply

Soroosh Sorooshian, Lot, Kher, and F. P. Sioshansi

Year: 1989
Volume: Volume 10
Number: Number 1
DOI: 10.5547/ISSN0195-6574-EJ-Vol10-No1-16
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Abstract:
[N]o one (except KSS, as far as I know) has recommended using the bonds approach for forecasting. KSS however, does not reply on a well-founded statistical basis for their forecasting procedure: it is decidedly ad hoc. We are pleased that Herbert has recognized the potential of "the bounds approach for forecasting" as a viable alternative for forecasting. His major concern is with the "ad hoc" nature of our forecasting procedure. This apparent occurs because forecasts generated by the NVM do not come with confidence intervals similar to those placed on forecasts generated by classical regression techniques. We wish to reply.





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